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Timezone: Europe/Vienna
 
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MON 13 JUL
8 a.m.
Affinity Workshop:
Women in Machine Learning Un-Workshop
(ends 12:00 AM)
10 a.m.
Tutorials 1 [10:00-12:30]
Machine Learning for Healthcare: Challenges, Methods, Frontiers
Machine Learning with Signal Processing
Representation Learning Without Labels
(duration 2.5 hr)
noon
Tutorials 2 [12:00-2:30]
Tutorials 2:00-5:00
Causal Reinforcement Learning
Epidemiology and Machine Learning
Parameter-free Online Optimization
Recent Advances in High-Dimensional Robust Statistics
(duration 2.5 hr)
1 p.m.
Affinity Workshop:
New In ML
(ends 6:00 PM)
Test Of Time:
Test of Time: Gaussian Process Optimization in the Bandit Settings: No Regret and Experimental Design
(duration 1.0 hr)
2 p.m.
Affinity Workshop:
LatinX in AI Workshop
(ends 2:15 AM)
5 p.m.
Tutorials 3 [5:00-7:30]
Bayesian Deep Learning and a Probabilistic Perspective of Model Construction
Model-Based Methods in Reinforcement Learning
Submodular Optimization: From Discrete to Continuous and Back
(duration 2.5 hr)
8 p.m.
Tutorials 1 [10:00-12:30]
Machine Learning for Healthcare: Challenges, Methods, Frontiers
Machine Learning with Signal Processing
Representation Learning Without Labels
(duration 2.5 hr)
10 p.m.
Tutorials 2 [12:00-2:30]
Tutorials 2:00-5:00
Causal Reinforcement Learning
Epidemiology and Machine Learning
Parameter-free Online Optimization
Recent Advances in High-Dimensional Robust Statistics
(duration 2.5 hr)
11 p.m.
Test Of Time:
Test of Time: Gaussian Process Optimization in the Bandit Settings: No Regret and Experimental Design
(duration 1.0 hr)
Award:
Welcome and Paper Awards
(ends 12:00 AM)
Affinity Workshop:
Queer in AI
(ends 11:00 AM)
poster session:
Joint Affinity Groups' Poster Session with Latinx in AI and Queer in AI
(ends 1:00 AM)

TUE 14 JUL
3 a.m.
Tutorials 3 [5:00-7:30]
Bayesian Deep Learning and a Probabilistic Perspective of Model Construction
Model-Based Methods in Reinforcement Learning
Submodular Optimization: From Discrete to Continuous and Back
(duration 2.5 hr)
2 p.m.
Invited Talk:
Doing Some Good with Machine Learning
Lester Mackey
(duration 2.0 hr)
4 p.m.
Poster Session 1 [4:00-4:45]
Posters 4:00-4:45
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
A Simple Framework for Contrastive Learning of Visual Representations
Combinatorial Pure Exploration for Dueling Bandit
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
ControlVAE: Controllable Variational Autoencoder
Data Amplification: Instance-Optimal Property Estimation
Distance Metric Learning with Joint Representation Diversification
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
Evaluating the Performance of Reinforcement Learning Algorithms
Fair Learning with Private Demographic Data
Familywise Error Rate Control by Interactive Unmasking
Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints
From Importance Sampling to Doubly Robust Policy Gradient
Improving Generative Imagination in Object-Centric World Models
Individual Fairness for k-Clustering
Laplacian Regularized Few-Shot Learning
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
Learning with Multiple Complementary Labels
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Mutual Transfer Learning for Massive Data
Nested Subspace Arrangement for Representation of Relational Data
On Implicit Regularization in $\beta$-VAEs
Private Reinforcement Learning with PAC and Regret Guarantees
Reverse-engineering deep ReLU networks
Self-Concordant Analysis of Frank-Wolfe Algorithms
Streaming k-Submodular Maximization under Noise subject to Size Constraint
What Can Learned Intrinsic Rewards Capture?
Accelerated Stochastic Gradient-free and Projection-free Methods
A Free-Energy Principle for Representation Learning
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions
Asynchronous Coagent Networks
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Causal Strategic Linear Regression
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
Collaborative Machine Learning with Incentive-Aware Model Rewards
Confidence-Aware Learning for Deep Neural Networks
Contrastive Multi-View Representation Learning on Graphs
Customizing ML Predictions for Online Algorithms
Deep Divergence Learning
Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning
DeltaGrad: Rapid retraining of machine learning models
Differentiating through the Fréchet Mean
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Efficient nonparametric statistical inference on population feature importance using Shapley values
Evaluating Lossy Compression Rates of Deep Generative Models
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms
Explaining Groups of Points in Low-Dimensional Representations
Fair Generative Modeling via Weak Supervision
Faster Graph Embeddings via Coarsening
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
FedBoost: A Communication-Efficient Algorithm for Federated Learning
FetchSGD: Communication-Efficient Federated Learning with Sketching
Full Law Identification in Graphical Models of Missing Data: Completeness Results
Generating Programmatic Referring Expressions via Program Synthesis
Generative Flows with Matrix Exponential
Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study
Improving the Gating Mechanism of Recurrent Neural Networks
Interpolation between Residual and Non-Residual Networks
Layered Sampling for Robust Optimization Problems
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Loss Function Search for Face Recognition
LTF: A Label Transformation Framework for Correcting Label Shift
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
Maximum-and-Concatenation Networks
Minimax Rate for Learning From Pairwise Comparisons in the BTL Model
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
Multigrid Neural Memory
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
Online Learning for Active Cache Synchronization
On Second-Order Group Influence Functions for Black-Box Predictions
On the Generalization Effects of Linear Transformations in Data Augmentation
On the Global Optimality of Model-Agnostic Meta-Learning
On the Noisy Gradient Descent that Generalizes as SGD
On Variational Learning of Controllable Representations for Text without Supervision
Optimizing for the Future in Non-Stationary MDPs
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Problems with Shapley-value-based explanations as feature importance measures
Provably Efficient Exploration in Policy Optimization
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Randomized Smoothing of All Shapes and Sizes
Recurrent Hierarchical Topic-Guided RNN for Language Generation
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Scalable Nearest Neighbor Search for Optimal Transport
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Semismooth Newton Algorithm for Efficient Projections onto $\ell_{1, \infty}$-norm Ball
Simple and Deep Graph Convolutional Networks
Stochastic Optimization for Non-convex Inf-Projection Problems
Stochastic Regret Minimization in Extensive-Form Games
Streaming Submodular Maximization under a k-Set System Constraint
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension
Taylor Expansion Policy Optimization
Tensor denoising and completion based on ordinal observations
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application
The Effect of Natural Distribution Shift on Question Answering Models
Towards Understanding the Dynamics of the First-Order Adversaries
Training Binary Neural Networks through Learning with Noisy Supervision
Two Routes to Scalable Credit Assignment without Weight Symmetry
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Variational Bayesian Quantization
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
Zeno++: Robust Fully Asynchronous SGD
(ends 4:45 PM)
5 p.m.
Poster Session 2 [5:00-5:45]
Posters 5:00-5:45
Adversarial Neural Pruning with Latent Vulnerability Suppression
An end-to-end approach for the verification problem: learning the right distance
Angular Visual Hardness
Closing the convergence gap of SGD without replacement
Discount Factor as a Regularizer in Reinforcement Learning
Estimating the Number and Effect Sizes of Non-null Hypotheses
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
Oracle Efficient Private Non-Convex Optimization
Stronger and Faster Wasserstein Adversarial Attacks
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
An EM Approach to Non-autoregressive Conditional Sequence Generation
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Batch Reinforcement Learning with Hyperparameter Gradients
Boosting for Control of Dynamical Systems
Detecting Out-of-Distribution Examples with Gram Matrices
Differentially Private Set Union
Distributed Online Optimization over a Heterogeneous Network
DROCC: Deep Robust One-Class Classification
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Feature Noise Induces Loss Discrepancy Across Groups
Finite-Time Convergence in Continuous-Time Optimization
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Invariant Risk Minimization Games
Is Local SGD Better than Minibatch SGD?
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
Meta-learning for Mixed Linear Regression
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures
Neural Clustering Processes
Nonparametric Score Estimators
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness
Overfitting in adversarially robust deep learning
Parameterized Rate-Distortion Stochastic Encoder
Parametric Gaussian Process Regressors
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Representations for Stable Off-Policy Reinforcement Learning
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Strategyproof Mean Estimation from Multiple-Choice Questions
The Tree Ensemble Layer: Differentiability meets Conditional Computation
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Working Memory Graphs
(ends 5:45 PM)
6 p.m.
Poster Session 3 [6:00-6:45]
Posters 6:00-6:45
A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
Disentangling Trainability and Generalization in Deep Neural Networks
Fast OSCAR and OWL Regression via Safe Screening Rules
Learning Algebraic Multigrid Using Graph Neural Networks
Low-loss connection of weight vectors: distribution-based approaches
Neural Contextual Bandits with UCB-based Exploration
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions
Recovery of Sparse Signals from a Mixture of Linear Samples
Scalable Deep Generative Modeling for Sparse Graphs
Streaming Coresets for Symmetric Tensor Factorization
Structured Policy Iteration for Linear Quadratic Regulator
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics
Tightening Exploration in Upper Confidence Reinforcement Learning
A Chance-Constrained Generative Framework for Sequence Optimization
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Being Bayesian about Categorical Probability
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Context Aware Local Differential Privacy
Deep k-NN for Noisy Labels
Description Based Text Classification with Reinforcement Learning
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Feature Quantization Improves GAN Training
Fiduciary Bandits
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Hierarchically Decoupled Imitation For Morphological Transfer
How Good is the Bayes Posterior in Deep Neural Networks Really?
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Learning and Sampling of Atomic Interventions from Observations
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning
Lookahead-Bounded Q-learning
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Nearly Linear Row Sampling Algorithm for Quantile Regression
Non-Autoregressive Neural Text-to-Speech
Obtaining Adjustable Regularization for Free via Iterate Averaging
On Semi-parametric Inference for BART
Provable guarantees for decision tree induction: the agnostic setting
Retrieval Augmented Language Model Pre-Training
Robustness to Spurious Correlations via Human Annotations
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Source Separation with Deep Generative Priors
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
The Many Shapley Values for Model Explanation
Two Simple Ways to Learn Individual Fairness Metrics from Data
Variable Skipping for Autoregressive Range Density Estimation
Variance Reduction in Stochastic Particle-Optimization Sampling
Variational Imitation Learning with Diverse-quality Demonstrations
(ends 6:45 PM)
7 p.m.
Poster Session 4 [7:00-7:45]
Posters 7:00-7:45
How recurrent networks implement contextual processing in sentiment analysis
Incremental Sampling Without Replacement for Sequence Models
Learning To Stop While Learning To Predict
On Learning Sets of Symmetric Elements
Revisiting Spatial Invariance with Low-Rank Local Connectivity
Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
A simpler approach to accelerated optimization: iterative averaging meets optimism
Automatic Shortcut Removal for Self-Supervised Representation Learning
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Deep Isometric Learning for Visual Recognition
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
Error Estimation for Sketched SVD via the Bootstrap
Explainable k-Means and k-Medians Clustering
Federated Learning with Only Positive Labels
Fully Parallel Hyperparameter Search: Reshaped Space-Filling
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Generative Pretraining From Pixels
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
Harmonic Decompositions of Convolutional Networks
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
Logarithmic Regret for Adversarial Online Control
Online Control of the False Coverage Rate and False Sign Rate
On the Theoretical Properties of the Network Jackknife
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Optimal Robust Learning of Discrete Distributions from Batches
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Selective Dyna-style Planning Under Limited Model Capacity
SoftSort: A Continuous Relaxation for the argsort Operator
Structured Prediction with Partial Labelling through the Infimum Loss
Topological Autoencoders
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Training Deep Energy-Based Models with f-Divergence Minimization
When are Non-Parametric Methods Robust?
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
(ends 7:45 PM)
8 p.m.
Poster Session 5 [8:00-8:45]
Posters 8:00-8:45
Adversarial Robustness for Code
LowFER: Low-rank Bilinear Pooling for Link Prediction
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Near-linear time Gaussian process optimization with adaptive batching and resparsification
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
Smaller, more accurate regression forests using tree alternating optimization
Understanding and Mitigating the Tradeoff between Robustness and Accuracy
When Explanations Lie: Why Many Modified BP Attributions Fail
A Swiss Army Knife for Minimax Optimal Transport
Bandits with Adversarial Scaling
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
Consistent Structured Prediction with Max-Min Margin Markov Networks
Domain Adaptive Imitation Learning
Explainable and Discourse Topic-aware Neural Language Understanding
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Information-Theoretic Local Minima Characterization and Regularization
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Learning Deep Kernels for Non-Parametric Two-Sample Tests
Online metric algorithms with untrusted predictions
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Quantum Expectation-Maximization for Gaussian mixture models
Stochastically Dominant Distributional Reinforcement Learning
Which Tasks Should Be Learned Together in Multi-task Learning?
(ends 8:45 PM)
9 p.m.
Poster Session 6 [9:00-9:45]
Posters 9:00-9:45
Bayesian Sparsification of Deep C-valued Networks
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
Entropy Minimization In Emergent Languages
Meta-Learning with Shared Amortized Variational Inference
Normalizing Flows on Tori and Spheres
Partial Trace Regression and Low-Rank Kraus Decomposition
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
TaskNorm: Rethinking Batch Normalization for Meta-Learning
A distributional view on multi-objective policy optimization
IPBoost – Non-Convex Boosting via Integer Programming
Learning disconnected manifolds: a no GAN's land
Learning Portable Representations for High-Level Planning
Learning the piece-wise constant graph structure of a varying Ising model
Learning to Simulate Complex Physics with Graph Networks
Multi-step Greedy Reinforcement Learning Algorithms
On Contrastive Learning for Likelihood-free Inference
Ready Policy One: World Building Through Active Learning
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
Weakly-Supervised Disentanglement Without Compromises
Word-Level Speech Recognition With a Letter to Word Encoder
(ends 9:45 PM)
10 p.m.
Poster Session 7 [10:00-10:45]
Posters 10:00-10:45
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
Constant Curvature Graph Convolutional Networks
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
Generalization to New Actions in Reinforcement Learning
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Adaptive Sampling for Estimating Probability Distributions
An Explicitly Relational Neural Network Architecture
Bayesian Differential Privacy for Machine Learning
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Graph Random Neural Features for Distance-Preserving Graph Representations
Growing Action Spaces
Implicit differentiation of Lasso-type models for hyperparameter optimization
Inexact Tensor Methods with Dynamic Accuracies
Learning with Good Feature Representations in Bandits and in RL with a Generative Model
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Meta-learning with Stochastic Linear Bandits
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Non-Stationary Delayed Bandits with Intermediate Observations
Online Convex Optimization in the Random Order Model
Optimal Randomized First-Order Methods for Least-Squares Problems
Predictive Sampling with Forecasting Autoregressive Models
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
Sequential Transfer in Reinforcement Learning with a Generative Model
StochasticRank: Global Optimization of Scale-Free Discrete Functions
Supervised learning: no loss no cry
Temporal Logic Point Processes
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture
(ends 10:45 PM)
11 p.m.
Poster Session 8 [11:00-11:45]
Posters 11:00-11:45
Debiased Sinkhorn barycenters
Improving Molecular Design by Stochastic Iterative Target Augmentation
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
Knowing The What But Not The Where in Bayesian Optimization
Learning Similarity Metrics for Numerical Simulations
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
On the Iteration Complexity of Hypergradient Computation
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation
Continuously Indexed Domain Adaptation
Decentralised Learning with Random Features and Distributed Gradient Descent
Extreme Multi-label Classification from Aggregated Labels
Fast Differentiable Sorting and Ranking
Finding trainable sparse networks through Neural Tangent Transfer
Infinite attention: NNGP and NTK for deep attention networks
Learning Reasoning Strategies in End-to-End Differentiable Proving
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
Optimal Estimator for Unlabeled Linear Regression
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Simple and sharp analysis of k-means||
Thompson Sampling via Local Uncertainty
Training Neural Networks for and by Interpolation
Voice Separation with an Unknown Number of Multiple Speakers
Why Are Learned Indexes So Effective?
Why bigger is not always better: on finite and infinite neural networks
(ends 11:45 PM)

WED 15 JUL
midnight
Poster Session 9 [12:00-12:45]
Posters 12:00-12:45
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Thompson Sampling Algorithms for Mean-Variance Bandits
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
Invariant Causal Prediction for Block MDPs
Unsupervised Speech Decomposition via Triple Information Bottleneck
When deep denoising meets iterative phase retrieval
(ends 12:45 AM)
1 a.m.
Invited Talk:
Doing Some Good with Machine Learning
Lester Mackey
(duration 2.0 hr)
3 a.m.
Poster Session 10 [3:00-3:45]
Posters 4:00-3:45
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
A Simple Framework for Contrastive Learning of Visual Representations
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
ControlVAE: Controllable Variational Autoencoder
Evaluating the Performance of Reinforcement Learning Algorithms
Fair Learning with Private Demographic Data
Familywise Error Rate Control by Interactive Unmasking
Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints
From Importance Sampling to Doubly Robust Policy Gradient
Improving Generative Imagination in Object-Centric World Models
Individual Fairness for k-Clustering
Laplacian Regularized Few-Shot Learning
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
Mutual Transfer Learning for Massive Data
Nested Subspace Arrangement for Representation of Relational Data
On Implicit Regularization in $\beta$-VAEs
Reverse-engineering deep ReLU networks
Streaming k-Submodular Maximization under Noise subject to Size Constraint
What Can Learned Intrinsic Rewards Capture?
A Free-Energy Principle for Representation Learning
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions
Asynchronous Coagent Networks
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Causal Strategic Linear Regression
Confidence-Aware Learning for Deep Neural Networks
Contrastive Multi-View Representation Learning on Graphs
Deep Divergence Learning
DeltaGrad: Rapid retraining of machine learning models
Differentiating through the Fréchet Mean
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Evaluating Lossy Compression Rates of Deep Generative Models
Explaining Groups of Points in Low-Dimensional Representations
Faster Graph Embeddings via Coarsening
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
FedBoost: A Communication-Efficient Algorithm for Federated Learning
FetchSGD: Communication-Efficient Federated Learning with Sketching
Full Law Identification in Graphical Models of Missing Data: Completeness Results
Generating Programmatic Referring Expressions via Program Synthesis
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Minimax Rate for Learning From Pairwise Comparisons in the BTL Model
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
Multigrid Neural Memory
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
Online Learning for Active Cache Synchronization
On the Generalization Effects of Linear Transformations in Data Augmentation
On the Global Optimality of Model-Agnostic Meta-Learning
On the Noisy Gradient Descent that Generalizes as SGD
Optimizing for the Future in Non-Stationary MDPs
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Provably Efficient Exploration in Policy Optimization
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Scalable Nearest Neighbor Search for Optimal Transport
Stochastic Optimization for Non-convex Inf-Projection Problems
Stochastic Regret Minimization in Extensive-Form Games
Streaming Submodular Maximization under a k-Set System Constraint
Taylor Expansion Policy Optimization
Tensor denoising and completion based on ordinal observations
The Effect of Natural Distribution Shift on Question Answering Models
Towards Understanding the Dynamics of the First-Order Adversaries
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Variational Bayesian Quantization
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Learning to Learn Kernels with Variational Random Features
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Efficiently Learning Adversarially Robust Halfspaces with Noise
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization
Intrinsic Reward Driven Imitation Learning via Generative Model
Learning De-biased Representations with Biased Representations
Learning with Feature and Distribution Evolvable Streams
Logistic Regression for Massive Data with Rare Events
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space
(ends 3:45 AM)
4 a.m.
Poster Session 11 [4:00-4:45]
Posters 4:00-4:45
Combinatorial Pure Exploration for Dueling Bandit
Distance Metric Learning with Joint Representation Diversification
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
Learning with Multiple Complementary Labels
LEEP: A New Measure to Evaluate Transferability of Learned Representations
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
Customizing ML Predictions for Online Algorithms
Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning
Efficient nonparametric statistical inference on population feature importance using Shapley values
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms
Improving the Gating Mechanism of Recurrent Neural Networks
Interpolation between Residual and Non-Residual Networks
Layered Sampling for Robust Optimization Problems
Loss Function Search for Face Recognition
LTF: A Label Transformation Framework for Correcting Label Shift
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
Maximum-and-Concatenation Networks
On Second-Order Group Influence Functions for Black-Box Predictions
On Variational Learning of Controllable Representations for Text without Supervision
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Problems with Shapley-value-based explanations as feature importance measures
Randomized Smoothing of All Shapes and Sizes
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Simple and Deep Graph Convolutional Networks
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application
Zeno++: Robust Fully Asynchronous SGD
An end-to-end approach for the verification problem: learning the right distance
Angular Visual Hardness
Estimating the Number and Effect Sizes of Non-null Hypotheses
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
Stronger and Faster Wasserstein Adversarial Attacks
An EM Approach to Non-autoregressive Conditional Sequence Generation
Batch Reinforcement Learning with Hyperparameter Gradients
Boosting for Control of Dynamical Systems
Detecting Out-of-Distribution Examples with Gram Matrices
Differentially Private Set Union
Distributed Online Optimization over a Heterogeneous Network
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Feature Noise Induces Loss Discrepancy Across Groups
Finite-Time Convergence in Continuous-Time Optimization
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Invariant Risk Minimization Games
Is Local SGD Better than Minibatch SGD?
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Neural Clustering Processes
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness
Overfitting in adversarially robust deep learning
Parametric Gaussian Process Regressors
Representations for Stable Off-Policy Reinforcement Learning
Strategyproof Mean Estimation from Multiple-Choice Questions
The Tree Ensemble Layer: Differentiability meets Conditional Computation
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
(ends 4:45 AM)
5 a.m.
Poster Session 12 [5:00-5:45]
Posters 4:00-6:45
Data Amplification: Instance-Optimal Property Estimation
Private Reinforcement Learning with PAC and Regret Guarantees
Self-Concordant Analysis of Frank-Wolfe Algorithms
Accelerated Stochastic Gradient-free and Projection-free Methods
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
Collaborative Machine Learning with Incentive-Aware Model Rewards
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Fair Generative Modeling via Weak Supervision
Generative Flows with Matrix Exponential
Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Recurrent Hierarchical Topic-Guided RNN for Language Generation
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Semismooth Newton Algorithm for Efficient Projections onto $\ell_{1, \infty}$-norm Ball
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension
Training Binary Neural Networks through Learning with Noisy Supervision
Two Routes to Scalable Credit Assignment without Weight Symmetry
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate
Parameterized Rate-Distortion Stochastic Encoder
Fast OSCAR and OWL Regression via Safe Screening Rules
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions
Scalable Deep Generative Modeling for Sparse Graphs
Structured Policy Iteration for Linear Quadratic Regulator
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics
A Chance-Constrained Generative Framework for Sequence Optimization
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Feature Quantization Improves GAN Training
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Hierarchically Decoupled Imitation For Morphological Transfer
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Learning and Sampling of Atomic Interventions from Observations
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning
Lookahead-Bounded Q-learning
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Nearly Linear Row Sampling Algorithm for Quantile Regression
Obtaining Adjustable Regularization for Free via Iterate Averaging
Provable guarantees for decision tree induction: the agnostic setting
Retrieval Augmented Language Model Pre-Training
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Source Separation with Deep Generative Priors
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
Variational Imitation Learning with Diverse-quality Demonstrations
(ends 5:45 AM)
6 a.m.
Poster Session 13 [6:00-6:45]
Posters 5:00-7:45
Adversarial Neural Pruning with Latent Vulnerability Suppression
Closing the convergence gap of SGD without replacement
Discount Factor as a Regularizer in Reinforcement Learning
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
Oracle Efficient Private Non-Convex Optimization
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
DROCC: Deep Robust One-Class Classification
Meta-learning for Mixed Linear Regression
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures
Nonparametric Score Estimators
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Working Memory Graphs
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Fiduciary Bandits
Two Simple Ways to Learn Individual Fairness Metrics from Data
Variable Skipping for Autoregressive Range Density Estimation
Incremental Sampling Without Replacement for Sequence Models
Learning To Stop While Learning To Predict
Revisiting Spatial Invariance with Low-Rank Local Connectivity
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Generative Pretraining From Pixels
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
Logarithmic Regret for Adversarial Online Control
Online Control of the False Coverage Rate and False Sign Rate
On the Theoretical Properties of the Network Jackknife
Optimal Robust Learning of Discrete Distributions from Batches
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Training Deep Energy-Based Models with f-Divergence Minimization
When are Non-Parametric Methods Robust?
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
(ends 6:45 AM)
7 a.m.
Poster Session 14 [7:00-7:45]
Posters 6:00-8:45
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
Disentangling Trainability and Generalization in Deep Neural Networks
Learning Algebraic Multigrid Using Graph Neural Networks
Low-loss connection of weight vectors: distribution-based approaches
Neural Contextual Bandits with UCB-based Exploration
Recovery of Sparse Signals from a Mixture of Linear Samples
Streaming Coresets for Symmetric Tensor Factorization
Tightening Exploration in Upper Confidence Reinforcement Learning
Being Bayesian about Categorical Probability
How Good is the Bayes Posterior in Deep Neural Networks Really?
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
Non-Autoregressive Neural Text-to-Speech
Robustness to Spurious Correlations via Human Annotations
The Many Shapley Values for Model Explanation
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
SoftSort: A Continuous Relaxation for the argsort Operator
Topological Autoencoders
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
Understanding and Mitigating the Tradeoff between Robustness and Accuracy
Bandits with Adversarial Scaling
Consistent Structured Prediction with Max-Min Margin Markov Networks
Domain Adaptive Imitation Learning
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Quantum Expectation-Maximization for Gaussian mixture models
Stochastically Dominant Distributional Reinforcement Learning
Which Tasks Should Be Learned Together in Multi-task Learning?
(ends 7:45 AM)
8 a.m.
Poster Session 15 [8:00-8:45]
Posters 7:00-9:45
How recurrent networks implement contextual processing in sentiment analysis
On Learning Sets of Symmetric Elements
Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning
A simpler approach to accelerated optimization: iterative averaging meets optimism
Automatic Shortcut Removal for Self-Supervised Representation Learning
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Deep Isometric Learning for Visual Recognition
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
Error Estimation for Sketched SVD via the Bootstrap
Explainable k-Means and k-Medians Clustering
Federated Learning with Only Positive Labels
Fully Parallel Hyperparameter Search: Reshaped Space-Filling
Harmonic Decompositions of Convolutional Networks
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Selective Dyna-style Planning Under Limited Model Capacity
Structured Prediction with Partial Labelling through the Infimum Loss
Adversarial Robustness for Code
When Explanations Lie: Why Many Modified BP Attributions Fail
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Learning Deep Kernels for Non-Parametric Two-Sample Tests
Online metric algorithms with untrusted predictions
Bayesian Sparsification of Deep C-valued Networks
Learning disconnected manifolds: a no GAN's land
Learning Portable Representations for High-Level Planning
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
Word-Level Speech Recognition With a Letter to Word Encoder
(ends 8:45 AM)
9 a.m.
Poster Session 16 [9:00-9:45]
Posters 8:00-10:45
LowFER: Low-rank Bilinear Pooling for Link Prediction
Near-linear time Gaussian process optimization with adaptive batching and resparsification
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer
Smaller, more accurate regression forests using tree alternating optimization
A Swiss Army Knife for Minimax Optimal Transport
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
Explainable and Discourse Topic-aware Neural Language Understanding
Information-Theoretic Local Minima Characterization and Regularization
Constant Curvature Graph Convolutional Networks
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
Adaptive Sampling for Estimating Probability Distributions
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Meta-learning with Stochastic Linear Bandits
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Optimal Randomized First-Order Methods for Least-Squares Problems
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
Supervised learning: no loss no cry
Temporal Logic Point Processes
(ends 9:45 AM)
10 a.m.
Poster Session 17 [10:00-10:45]
Posters 9:00-11:45
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
Entropy Minimization In Emergent Languages
Meta-Learning with Shared Amortized Variational Inference
Normalizing Flows on Tori and Spheres
Partial Trace Regression and Low-Rank Kraus Decomposition
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
TaskNorm: Rethinking Batch Normalization for Meta-Learning
A distributional view on multi-objective policy optimization
IPBoost – Non-Convex Boosting via Integer Programming
Learning the piece-wise constant graph structure of a varying Ising model
Learning to Simulate Complex Physics with Graph Networks
Multi-step Greedy Reinforcement Learning Algorithms
On Contrastive Learning for Likelihood-free Inference
Ready Policy One: World Building Through Active Learning
Weakly-Supervised Disentanglement Without Compromises
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Generalization to New Actions in Reinforcement Learning
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
An Explicitly Relational Neural Network Architecture
Bayesian Differential Privacy for Machine Learning
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Implicit differentiation of Lasso-type models for hyperparameter optimization
Inexact Tensor Methods with Dynamic Accuracies
Non-Stationary Delayed Bandits with Intermediate Observations
Predictive Sampling with Forecasting Autoregressive Models
Improving Molecular Design by Stochastic Iterative Target Augmentation
Learning Similarity Metrics for Numerical Simulations
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
Finding trainable sparse networks through Neural Tangent Transfer
Infinite attention: NNGP and NTK for deep attention networks
Learning Reasoning Strategies in End-to-End Differentiable Proving
Optimal Estimator for Unlabeled Linear Regression
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Why Are Learned Indexes So Effective?
(ends 10:45 AM)
11 a.m.
Poster Session 18 [11:00-11:45]
Posters 10:00-11:45
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Graph Random Neural Features for Distance-Preserving Graph Representations
Growing Action Spaces
Learning with Good Feature Representations in Bandits and in RL with a Generative Model
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Online Convex Optimization in the Random Order Model
Sequential Transfer in Reinforcement Learning with a Generative Model
StochasticRank: Global Optimization of Scale-Free Discrete Functions
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture
Thompson Sampling via Local Uncertainty
Training Neural Networks for and by Interpolation
Why bigger is not always better: on finite and infinite neural networks
(ends 11:45 AM)
noon
Poster Session 19 [12:00-12:45]
Posters 11:00-3:45
Debiased Sinkhorn barycenters
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
Knowing The What But Not The Where in Bayesian Optimization
On the Iteration Complexity of Hypergradient Computation
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation
Continuously Indexed Domain Adaptation
Decentralised Learning with Random Features and Distributed Gradient Descent
Extreme Multi-label Classification from Aggregated Labels
Fast Differentiable Sorting and Ranking
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
Simple and sharp analysis of k-means||
Voice Separation with an Unknown Number of Multiple Speakers
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
(ends 12:45 PM)
1 p.m.
Poster Session 20 [1:00-1:45]
Posters 12:00-4:45
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Thompson Sampling Algorithms for Mean-Variance Bandits
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
Invariant Causal Prediction for Block MDPs
Unsupervised Speech Decomposition via Triple Information Bottleneck
When deep denoising meets iterative phase retrieval
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Learning to Learn Kernels with Variational Random Features
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Efficiently Learning Adversarially Robust Halfspaces with Noise
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization
Intrinsic Reward Driven Imitation Learning via Generative Model
Learning De-biased Representations with Biased Representations
Learning with Feature and Distribution Evolvable Streams
Logistic Regression for Massive Data with Rare Events
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
(ends 1:45 PM)
2 p.m.
Poster Session 21 [2:00-2:45]
Posters 2:00-2:45
Abstraction Mechanisms Predict Generalization in Deep Neural Networks
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
An Optimistic Perspective on Offline Deep Reinforcement Learning
Consistent Estimators for Learning to Defer to an Expert
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
Fair k-Centers via Maximum Matching
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Hierarchical Generation of Molecular Graphs using Structural Motifs
Learnable Group Transform For Time-Series
Learning Opinions in Social Networks
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Median Matrix Completion: from Embarrassment to Optimality
Message Passing Least Squares Framework and its Application to Rotation Synchronization
Optimal approximation for unconstrained non-submodular minimization
Optimal Bounds between f-Divergences and Integral Probability Metrics
Preference Modeling with Context-Dependent Salient Features
Privately Learning Markov Random Fields
Robust and Stable Black Box Explanations
Second-Order Provable Defenses against Adversarial Attacks
The Differentiable Cross-Entropy Method
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
Adaptive Estimator Selection for Off-Policy Evaluation
Adversarial Risk via Optimal Transport and Optimal Couplings
An Accelerated DFO Algorithm for Finite-sum Convex Functions
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
Causal Effect Identifiability under Partial-Observability
Causal Modeling for Fairness In Dynamical Systems
Channel Equilibrium Networks for Learning Deep Representation
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions
Data preprocessing to mitigate bias: A maximum entropy based approach
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy
Efficient Intervention Design for Causal Discovery with Latents
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
Identifying Statistical Bias in Dataset Replication
Implicit Generative Modeling for Efficient Exploration
Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study
InstaHide: Instance-hiding Schemes for Private Distributed Learning
Label-Noise Robust Domain Adaptation
Learning Quadratic Games on Networks
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Learning What to Defer for Maximum Independent Sets
Moniqua: Modulo Quantized Communication in Decentralized SGD
Neural Architecture Search in A Proxy Validation Loss Landscape
One Size Fits All: Can We Train One Denoiser for All Noise Levels?
On Learning Language-Invariant Representations for Universal Machine Translation
Online Bayesian Moment Matching based SAT Solver Heuristics
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies
Operation-Aware Soft Channel Pruning using Differentiable Masks
Population-Based Black-Box Optimization for Biological Sequence Design
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Private Query Release Assisted by Public Data
Progressive Graph Learning for Open-Set Domain Adaptation
Reinforcement Learning for Integer Programming: Learning to Cut
Robust Bayesian Classification Using An Optimistic Score Ratio
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
Sequential Cooperative Bayesian Inference
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Stabilizing Transformers for Reinforcement Learning
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data
The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Variational Label Enhancement
What can I do here? A Theory of Affordances in Reinforcement Learning
(ends 2:45 PM)
3 p.m.
Invited Talk:
Human and Machine Learning for Assistive Autonomy
Brenna Argall
(duration 2.0 hr)
5 p.m.
Poster Session 22 [5:00-5:45]
Posters 5:00-5:45
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Choice Set Optimization Under Discrete Choice Models of Group Decisions
Class-Weighted Classification: Trade-offs and Robust Approaches
Efficient Policy Learning from Surrogate-Loss Classification Reductions
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent
Frustratingly Simple Few-Shot Object Detection
Inductive Relation Prediction by Subgraph Reasoning
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Learning and Evaluating Contextual Embedding of Source Code
On conditional versus marginal bias in multi-armed bandits
On Coresets for Regularized Regression
Optimizing Black-box Metrics with Adaptive Surrogates
Planning to Explore via Self-Supervised World Models
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Sparse Shrunk Additive Models
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
The Non-IID Data Quagmire of Decentralized Machine Learning
Adversarial Filters of Dataset Biases
Adversarial Mutual Information for Text Generation
A general recurrent state space framework for modeling neural dynamics during decision-making
Alleviating Privacy Attacks via Causal Learning
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
Approximation Capabilities of Neural ODEs and Invertible Residual Networks
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
Black-box Certification and Learning under Adversarial Perturbations
Boosting Deep Neural Network Efficiency with Dual-Module Inference
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
Budgeted Online Influence Maximization
Circuit-Based Intrinsic Methods to Detect Overfitting
Communication-Efficient Distributed PCA by Riemannian Optimization
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Coresets for Clustering in Graphs of Bounded Treewidth
Decoupled Greedy Learning of CNNs
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Identifying the Reward Function by Anchor Actions
Imputer: Sequence Modelling via Imputation and Dynamic Programming
Individual Calibration with Randomized Forecasting
Learning to Score Behaviors for Guided Policy Optimization
(Locally) Differentially Private Combinatorial Semi-Bandits
Negative Sampling in Semi-Supervised learning
Online mirror descent and dual averaging: keeping pace in the dynamic case
Optimal transport mapping via input convex neural networks
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
Predicting deliberative outcomes
Predictive Multiplicity in Classification
Private Outsourced Bayesian Optimization
Proper Network Interpretability Helps Adversarial Robustness in Classification
Randomly Projected Additive Gaussian Processes for Regression
Representing Unordered Data Using Complex-Weighted Multiset Automata
Robust Outlier Arm Identification
Safe screening rules for L0-regression from Perspective Relaxations
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Self-Modulating Nonparametric Event-Tensor Factorization
Sets Clustering
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
Stochastic Gradient and Langevin Processes
Strength from Weakness: Fast Learning Using Weak Supervision
Structure Adaptive Algorithms for Stochastic Bandits
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Understanding Self-Training for Gradual Domain Adaptation
Undirected Graphical Models as Approximate Posteriors
(ends 5:45 PM)
6 p.m.
Poster Session 23 [6:00-6:45]
Posters 6:00-6:45
Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Structural Language Models of Code
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
Adversarial Robustness Against the Union of Multiple Perturbation Models
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Cooperative Multi-Agent Bandits with Heavy Tails
Dual Mirror Descent for Online Allocation Problems
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations
Flexible and Efficient Long-Range Planning Through Curious Exploration
Hallucinative Topological Memory for Zero-Shot Visual Planning
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Margin-aware Adversarial Domain Adaptation with Optimal Transport
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
On the consistency of top-k surrogate losses
Optimal Sequential Maximization: One Interview is Enough!
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers
Ordinal Non-negative Matrix Factorization for Recommendation
Provable Self-Play Algorithms for Competitive Reinforcement Learning
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Set Functions for Time Series
(ends 6:45 PM)
7 p.m.
Poster Session 24 [7:00-7:45]
Posters 7:00-7:45
Data Valuation using Reinforcement Learning
Hypernetwork approach to generating point clouds
Learning to Simulate and Design for Structural Engineering
NetGAN without GAN: From Random Walks to Low-Rank Approximations
Quantized Decentralized Stochastic Learning over Directed Graphs
Self-supervised Label Augmentation via Input Transformations
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Time-Consistent Self-Supervision for Semi-Supervised Learning
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
A Game Theoretic Framework for Model Based Reinforcement Learning
Batch Stationary Distribution Estimation
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
Continuous-time Lower Bounds for Gradient-based Algorithms
Correlation Clustering with Asymmetric Classification Errors
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Efficiently Solving MDPs with Stochastic Mirror Descent
Evolutionary Topology Search for Tensor Network Decomposition
Fast computation of Nash Equilibria in Imperfect Information Games
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Hierarchical Verification for Adversarial Robustness
How to Solve Fair k-Center in Massive Data Models
Learning Calibratable Policies using Programmatic Style-Consistency
Learning Representations that Support Extrapolation
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks
Provable Representation Learning for Imitation Learning via Bi-level Optimization
Single Point Transductive Prediction
Small-GAN: Speeding up GAN Training using Core-Sets
Stochastic bandits with arm-dependent delays
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons
(ends 7:45 PM)
8 p.m.
Poster Session 25 [8:00-8:45]
Posters 8:00-8:45
Adaptive Gradient Descent without Descent
From Local SGD to Local Fixed-Point Methods for Federated Learning
On the Number of Linear Regions of Convolutional Neural Networks
Option Discovery in the Absence of Rewards with Manifold Analysis
Revisiting Fundamentals of Experience Replay
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
A Geometric Approach to Archetypal Analysis via Sparse Projections
DeBayes: a Bayesian Method for Debiasing Network Embeddings
Deep Gaussian Markov Random Fields
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime
Encoding Musical Style with Transformer Autoencoders
Fairwashing explanations with off-manifold detergent
Feature Selection using Stochastic Gates
Gamification of Pure Exploration for Linear Bandits
Learning Near Optimal Policies with Low Inherent Bellman Error
Multidimensional Shape Constraints
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
“Other-Play” for Zero-Shot Coordination
PackIt: A Virtual Environment for Geometric Planning
PolyGen: An Autoregressive Generative Model of 3D Meshes
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Robust Graph Representation Learning via Neural Sparsification
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
The Implicit and Explicit Regularization Effects of Dropout
The Shapley Taylor Interaction Index
(ends 8:45 PM)
9 p.m.
Poster Session 26 [9:00-9:45]
Posters 9:00-9:45
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition
Adversarial Nonnegative Matrix Factorization
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
k-means++: few more steps yield constant approximation
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
Scalable Exact Inference in Multi-Output Gaussian Processes
Too Relaxed to Be Fair
A new regret analysis for Adam-type algorithms
Automatic Reparameterisation of Probabilistic Programs
CoMic: Complementary Task Learning & Mimicry for Reusable Skills
Convergence Rates of Variational Inference in Sparse Deep Learning
Curvature-corrected learning dynamics in deep neural networks
Deep Coordination Graphs
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Equivariant Neural Rendering
Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Implicit Regularization of Random Feature Models
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Learning to Encode Position for Transformer with Continuous Dynamical Model
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits
No-Regret Exploration in Goal-Oriented Reinforcement Learning
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
Optimal Continual Learning has Perfect Memory and is NP-hard
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Radioactive data: tracing through training
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
T-Basis: a Compact Representation for Neural Networks
Towards a General Theory of Infinite-Width Limits of Neural Classifiers
Training Linear Neural Networks: Non-Local Convergence and Complexity Results
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time
Unique Properties of Flat Minima in Deep Networks
(ends 9:45 PM)
10 p.m.
Poster Session 27 [10:00-10:45]
Posters 10:00-10:45
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Boosting Frank-Wolfe by Chasing Gradients
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization
Fast Adaptation to New Environments via Policy-Dynamics Value Functions
Generalisation error in learning with random features and the hidden manifold model
Interference and Generalization in Temporal Difference Learning
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
Leveraging Frequency Analysis for Deep Fake Image Recognition
On the Generalization Benefit of Noise in Stochastic Gradient Descent
On the Sample Complexity of Adversarial Multi-Source PAC Learning
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Adding seemingly uninformative labels helps in low data regimes
A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits
Attentive Group Equivariant Convolutional Networks
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Continuous Time Bayesian Networks with Clocks
Discriminative Adversarial Search for Abstractive Summarization
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
Growing Adaptive Multi-hyperplane Machines
Implicit Geometric Regularization for Learning Shapes
Involutive MCMC: a Unifying Framework
Quantum Boosting
Scalable and Efficient Comparison-based Search without Features
Self-Attentive Hawkes Process
Super-efficiency of automatic differentiation for functions defined as a minimum
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Up or Down? Adaptive Rounding for Post-Training Quantization
(ends 10:45 PM)
11 p.m.
Poster Session 28 [11:00-11:45]
Posters 11:00-11:45
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
Explicit Gradient Learning for Black-Box Optimization
It's Not What Machines Can Learn, It's What We Cannot Teach
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs
Sparse Gaussian Processes with Spherical Harmonic Features
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing
DeepCoDA: personalized interpretability for compositional health data
Healing Products of Gaussian Process Experts
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Lifted Disjoint Paths with Application in Multiple Object Tracking
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Small Data, Big Decisions: Model Selection in the Small-Data Regime
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
(ends 11:45 PM)

THU 16 JUL
midnight
Poster Session 29 [12:00-12:45]
Posters 12:00-12:45
Haar Graph Pooling
MetaFun: Meta-Learning with Iterative Functional Updates
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
Agent57: Outperforming the Atari Human Benchmark
DINO: Distributed Newton-Type Optimization Method
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
Statistically Efficient Off-Policy Policy Gradients
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
(ends 12:45 AM)
1 a.m.
Poster Session 30 [1:00-1:45]
Posters 2:00-1:45
Abstraction Mechanisms Predict Generalization in Deep Neural Networks
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
An Optimistic Perspective on Offline Deep Reinforcement Learning
Consistent Estimators for Learning to Defer to an Expert
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
Fair k-Centers via Maximum Matching
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Hierarchical Generation of Molecular Graphs using Structural Motifs
Learnable Group Transform For Time-Series
Learning Opinions in Social Networks
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Message Passing Least Squares Framework and its Application to Rotation Synchronization
Optimal Bounds between f-Divergences and Integral Probability Metrics
Preference Modeling with Context-Dependent Salient Features
Privately Learning Markov Random Fields
Robust and Stable Black Box Explanations
Second-Order Provable Defenses against Adversarial Attacks
The Differentiable Cross-Entropy Method
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
Adaptive Estimator Selection for Off-Policy Evaluation
Adversarial Risk via Optimal Transport and Optimal Couplings
Causal Effect Identifiability under Partial-Observability
Causal Modeling for Fairness In Dynamical Systems
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
Data preprocessing to mitigate bias: A maximum entropy based approach
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy
Efficient Intervention Design for Causal Discovery with Latents
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
Identifying Statistical Bias in Dataset Replication
Implicit Generative Modeling for Efficient Exploration
Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study
InstaHide: Instance-hiding Schemes for Private Distributed Learning
Learning Quadratic Games on Networks
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Moniqua: Modulo Quantized Communication in Decentralized SGD
One Size Fits All: Can We Train One Denoiser for All Noise Levels?
On Learning Language-Invariant Representations for Universal Machine Translation
Online Bayesian Moment Matching based SAT Solver Heuristics
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies
Population-Based Black-Box Optimization for Biological Sequence Design
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Private Query Release Assisted by Public Data
Reinforcement Learning for Integer Programming: Learning to Cut
Robust Bayesian Classification Using An Optimistic Score Ratio
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
Sequential Cooperative Bayesian Inference
Stabilizing Transformers for Reinforcement Learning
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data
The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
What can I do here? A Theory of Affordances in Reinforcement Learning
Adversarial Robustness via Runtime Masking and Cleansing
Bidirectional Model-based Policy Optimization
From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Safe Reinforcement Learning in Constrained Markov Decision Processes
Automated Synthetic-to-Real Generalization
Boosted Histogram Transform for Regression
Feature-map-level Online Adversarial Knowledge Distillation
Non-separable Non-stationary random fields
Online Dense Subgraph Discovery via Blurred-Graph Feedback
On Lp-norm Robustness of Ensemble Decision Stumps and Trees
On Relativistic f-Divergences
Optimization from Structured Samples for Coverage Functions
Spread Divergence
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
(ends 1:45 AM)
2 a.m.
Invited Talk:
Human and Machine Learning for Assistive Autonomy
Brenna Argall
(duration 2.0 hr)
4 a.m.
Poster Session 31 [4:00-4:45]
Posters 2:00-5:45
Median Matrix Completion: from Embarrassment to Optimality
Optimal approximation for unconstrained non-submodular minimization
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
Channel Equilibrium Networks for Learning Deep Representation
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions
Label-Noise Robust Domain Adaptation
Learning What to Defer for Maximum Independent Sets
Neural Architecture Search in A Proxy Validation Loss Landscape
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
Operation-Aware Soft Channel Pruning using Differentiable Masks
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Efficient Policy Learning from Surrogate-Loss Classification Reductions
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent
Frustratingly Simple Few-Shot Object Detection
Inductive Relation Prediction by Subgraph Reasoning
Learning and Evaluating Contextual Embedding of Source Code
On conditional versus marginal bias in multi-armed bandits
Planning to Explore via Self-Supervised World Models
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Sparse Shrunk Additive Models
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Adversarial Mutual Information for Text Generation
A general recurrent state space framework for modeling neural dynamics during decision-making
Approximation Capabilities of Neural ODEs and Invertible Residual Networks
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
Black-box Certification and Learning under Adversarial Perturbations
Boosting Deep Neural Network Efficiency with Dual-Module Inference
Budgeted Online Influence Maximization
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Decoupled Greedy Learning of CNNs
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
Identifying the Reward Function by Anchor Actions
Imputer: Sequence Modelling via Imputation and Dynamic Programming
Individual Calibration with Randomized Forecasting
Learning to Score Behaviors for Guided Policy Optimization
Negative Sampling in Semi-Supervised learning
Online mirror descent and dual averaging: keeping pace in the dynamic case
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
Predicting deliberative outcomes
Predictive Multiplicity in Classification
Private Outsourced Bayesian Optimization
Representing Unordered Data Using Complex-Weighted Multiset Automata
Robust Outlier Arm Identification
Safe screening rules for L0-regression from Perspective Relaxations
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
(ends 4:45 AM)
5 a.m.
Poster Session 32 [5:00-5:45]
Posters 6:00-6:45
A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change
Context Aware Local Differential Privacy
Deep k-NN for Noisy Labels
Description Based Text Classification with Reinforcement Learning
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
On Semi-parametric Inference for BART
Variance Reduction in Stochastic Particle-Optimization Sampling
An Accelerated DFO Algorithm for Finite-sum Convex Functions
Progressive Graph Learning for Open-Set Domain Adaptation
Variational Label Enhancement
Choice Set Optimization Under Discrete Choice Models of Group Decisions
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
On Coresets for Regularized Regression
Adversarial Filters of Dataset Biases
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
Communication-Efficient Distributed PCA by Riemannian Optimization
Coresets for Clustering in Graphs of Bounded Treewidth
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Optimal transport mapping via input convex neural networks
Proper Network Interpretability Helps Adversarial Robustness in Classification
Self-Modulating Nonparametric Event-Tensor Factorization
Sets Clustering
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Understanding Self-Training for Gradual Domain Adaptation
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
Cooperative Multi-Agent Bandits with Heavy Tails
Dual Mirror Descent for Online Allocation Problems
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Leveraging Procedural Generation to Benchmark Reinforcement Learning
On the consistency of top-k surrogate losses
Optimal Sequential Maximization: One Interview is Enough!
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
(ends 5:45 AM)
6 a.m.
Poster Session 33 [6:00-6:45]
Posters 5:00-7:45
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Class-Weighted Classification: Trade-offs and Robust Approaches
Optimizing Black-box Metrics with Adaptive Surrogates
The Non-IID Data Quagmire of Decentralized Machine Learning
Alleviating Privacy Attacks via Causal Learning
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
Circuit-Based Intrinsic Methods to Detect Overfitting
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
(Locally) Differentially Private Combinatorial Semi-Bandits
Randomly Projected Additive Gaussian Processes for Regression
Stochastic Gradient and Langevin Processes
Strength from Weakness: Fast Learning Using Weak Supervision
Structure Adaptive Algorithms for Stochastic Bandits
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Undirected Graphical Models as Approximate Posteriors
Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Adversarial Robustness Against the Union of Multiple Perturbation Models
Hallucinative Topological Memory for Zero-Shot Visual Planning
Data Valuation using Reinforcement Learning
Quantized Decentralized Stochastic Learning over Directed Graphs
A Game Theoretic Framework for Model Based Reinforcement Learning
Batch Stationary Distribution Estimation
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
Correlation Clustering with Asymmetric Classification Errors
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Efficiently Solving MDPs with Stochastic Mirror Descent
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Hierarchical Verification for Adversarial Robustness
Learning Calibratable Policies using Programmatic Style-Consistency
Learning Representations that Support Extrapolation
Provable Representation Learning for Imitation Learning via Bi-level Optimization
Single Point Transductive Prediction
Small-GAN: Speeding up GAN Training using Core-Sets
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons
(ends 6:45 AM)
7 a.m.
Poster Session 34 [7:00-7:45]
Posters 6:00-8:45
Structural Language Models of Code
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations
Flexible and Efficient Long-Range Planning Through Curious Exploration
Margin-aware Adversarial Domain Adaptation with Optimal Transport
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
Ordinal Non-negative Matrix Factorization for Recommendation
Provable Self-Play Algorithms for Competitive Reinforcement Learning
Set Functions for Time Series
Learning to Simulate and Design for Structural Engineering
NetGAN without GAN: From Random Walks to Low-Rank Approximations
Continuous-time Lower Bounds for Gradient-based Algorithms
How to Solve Fair k-Center in Massive Data Models
On the Number of Linear Regions of Convolutional Neural Networks
Revisiting Fundamentals of Experience Replay
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Deep Gaussian Markov Random Fields
Encoding Musical Style with Transformer Autoencoders
Learning Near Optimal Policies with Low Inherent Bellman Error
Multidimensional Shape Constraints
“Other-Play” for Zero-Shot Coordination
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Robust Graph Representation Learning via Neural Sparsification
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
The Implicit and Explicit Regularization Effects of Dropout
The Shapley Taylor Interaction Index
(ends 7:45 AM)
8 a.m.
Poster Session 35 [8:00-8:45]
Posters 7:00-9:45
Hypernetwork approach to generating point clouds
Self-supervised Label Augmentation via Input Transformations
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Time-Consistent Self-Supervision for Semi-Supervised Learning
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Evolutionary Topology Search for Tensor Network Decomposition
Fast computation of Nash Equilibria in Imperfect Information Games
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks
Stochastic bandits with arm-dependent delays
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
Option Discovery in the Absence of Rewards with Manifold Analysis
DeBayes: a Bayesian Method for Debiasing Network Embeddings
Feature Selection using Stochastic Gates
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Adversarial Nonnegative Matrix Factorization
Too Relaxed to Be Fair
Curvature-corrected learning dynamics in deep neural networks
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
Learning to Encode Position for Transformer with Continuous Dynamical Model
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Towards a General Theory of Infinite-Width Limits of Neural Classifiers
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time
(ends 8:45 AM)
9 a.m.
Poster Session 36 [9:00-9:45]
Posters 8:00-10:45
Adaptive Gradient Descent without Descent
From Local SGD to Local Fixed-Point Methods for Federated Learning
A Geometric Approach to Archetypal Analysis via Sparse Projections
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime
Fairwashing explanations with off-manifold detergent
Gamification of Pure Exploration for Linear Bandits
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
PackIt: A Virtual Environment for Geometric Planning
PolyGen: An Autoregressive Generative Model of 3D Meshes
A new regret analysis for Adam-type algorithms
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Training Linear Neural Networks: Non-Local Convergence and Complexity Results
Boosting Frank-Wolfe by Chasing Gradients
Attentive Group Equivariant Convolutional Networks
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
Up or Down? Adaptive Rounding for Post-Training Quantization
(ends 9:45 AM)
10 a.m.
Poster Session 37 [10:00-10:45]
Posters 9:00-11:45
Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
k-means++: few more steps yield constant approximation
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
Scalable Exact Inference in Multi-Output Gaussian Processes
Automatic Reparameterisation of Probabilistic Programs
CoMic: Complementary Task Learning & Mimicry for Reusable Skills
Convergence Rates of Variational Inference in Sparse Deep Learning
Deep Coordination Graphs
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Equivariant Neural Rendering
Implicit Regularization of Random Feature Models
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
No-Regret Exploration in Goal-Oriented Reinforcement Learning
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
Optimal Continual Learning has Perfect Memory and is NP-hard
Radioactive data: tracing through training
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
T-Basis: a Compact Representation for Neural Networks
Unique Properties of Flat Minima in Deep Networks
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
On the Generalization Benefit of Noise in Stochastic Gradient Descent
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Adding seemingly uninformative labels helps in low data regimes
Discriminative Adversarial Search for Abstractive Summarization
Involutive MCMC: a Unifying Framework
Self-Attentive Hawkes Process
Super-efficiency of automatic differentiation for functions defined as a minimum
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Explicit Gradient Learning for Black-Box Optimization
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs
Sparse Gaussian Processes with Spherical Harmonic Features
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing
DeepCoDA: personalized interpretability for compositional health data
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
(ends 10:45 AM)
11 a.m.
Poster Session 38 [11:00-11:45]
Posters 10:00-12:45
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization
Fast Adaptation to New Environments via Policy-Dynamics Value Functions
Generalisation error in learning with random features and the hidden manifold model
Interference and Generalization in Temporal Difference Learning
Leveraging Frequency Analysis for Deep Fake Image Recognition
On the Sample Complexity of Adversarial Multi-Source PAC Learning
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Continuous Time Bayesian Networks with Clocks
Growing Adaptive Multi-hyperplane Machines
Implicit Geometric Regularization for Learning Shapes
Quantum Boosting
Scalable and Efficient Comparison-based Search without Features
Haar Graph Pooling
MetaFun: Meta-Learning with Iterative Functional Updates
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
Agent57: Outperforming the Atari Human Benchmark
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
(ends 11:45 AM)
noon
Poster Session 39 [12:00-12:45]
Posters 11:00-1:45
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
It's Not What Machines Can Learn, It's What We Cannot Teach
Healing Products of Gaussian Process Experts
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Lifted Disjoint Paths with Application in Multiple Object Tracking
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Small Data, Big Decisions: Model Selection in the Small-Data Regime
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
Bidirectional Model-based Policy Optimization
Boosted Histogram Transform for Regression
Non-separable Non-stationary random fields
On Lp-norm Robustness of Ensemble Decision Stumps and Trees
On Relativistic f-Divergences
Optimization from Structured Samples for Coverage Functions
Spread Divergence
(ends 12:45 PM)
1 p.m.
Poster Session 40 [1:00-1:45]
Posters 12:00-1:45
DINO: Distributed Newton-Type Optimization Method
Statistically Efficient Off-Policy Policy Gradients
Adversarial Robustness via Runtime Masking and Cleansing
From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Safe Reinforcement Learning in Constrained Markov Decision Processes
Automated Synthetic-to-Real Generalization
Feature-map-level Online Adversarial Knowledge Distillation
Online Dense Subgraph Discovery via Blurred-Graph Feedback
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
(ends 1:45 PM)
2 p.m.
Town Hall:
Town Hall Meeting
(duration 1.0 hr)
3 p.m.
Poster Session 41 [3:00-3:45]
Posters 3:00-3:45
Acceleration through spectral density estimation
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Adaptive Adversarial Multi-task Representation Learning
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
Born-again Tree Ensembles
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Differentiable Product Quantization for End-to-End Embedding Compression
Do GANs always have Nash equilibria?
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Enhancing Simple Models by Exploiting What They Already Know
Error-Bounded Correction of Noisy Labels
FACT: A Diagnostic for Group Fairness Trade-offs
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Improving Robustness of Deep-Learning-Based Image Reconstruction
Improving Transformer Optimization Through Better Initialization
Learning Selection Strategies in Buchberger’s Algorithm
Learning the Valuations of a $k$-demand Agent
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
New Oracle-Efficient Algorithms for Private Synthetic Data Release
On a projective ensemble approach to two sample test for equality of distributions
p-Norm Flow Diffusion for Local Graph Clustering
Privately detecting changes in unknown distributions
Reducing Sampling Error in Batch Temporal Difference Learning
Robust Pricing in Dynamic Mechanism Design
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Stochastic Flows and Geometric Optimization on the Orthogonal Group
Task Understanding from Confusing Multi-task Data
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching
Universal Asymptotic Optimality of Polyak Momentum
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Adaptive Region-Based Active Learning
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Almost Tune-Free Variance Reduction
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Bandits for BMO Functions
BINOCULARS for efficient, nonmyopic sequential experimental design
Black-Box Methods for Restoring Monotonicity
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Bridging the Gap Between f-GANs and Wasserstein GANs
Calibration, Entropy Rates, and Memory in Language Models
Causal Inference using Gaussian Processes with Structured Latent Confounders
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Concise Explanations of Neural Networks using Adversarial Training
Constrained Markov Decision Processes via Backward Value Functions
Continuous Graph Neural Networks
Convex Calibrated Surrogates for the Multi-Label F-Measure
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Countering Language Drift with Seeded Iterated Learning
Deep Reinforcement Learning with Smooth Policy
Does label smoothing mitigate label noise?
Do RNN and LSTM have Long Memory?
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Estimation of Bounds on Potential Outcomes For Decision Making
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Graph Homomorphism Convolution
GraphOpt: Learning Optimization Models of Graph Formation
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Input-Sparsity Low Rank Approximation in Schatten Norm
Interpreting Robust Optimization via Adversarial Influence Functions
Invariant Rationalization
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Latent Variable Modelling with Hyperbolic Normalizing Flows
Learning Optimal Tree Models under Beam Search
Learning Robot Skills with Temporal Variational Inference
Learning with Bounded Instance- and Label-dependent Label Noise
Linear Lower Bounds and Conditioning of Differentiable Games
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems
Low-Rank Bottleneck in Multi-head Attention Models
Mapping natural-language problems to formal-language solutions using structured neural representations
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Meta Variance Transfer: Learning to Augment from the Others
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Multi-Agent Routing Value Iteration Network
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
On Breaking Deep Generative Model-based Defenses and Beyond
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Online Learning with Dependent Stochastic Feedback Graphs
On the Expressivity of Neural Networks for Deep Reinforcement Learning
Optimizing Dynamic Structures with Bayesian Generative Search
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Performative Prediction
Piecewise Linear Regression via a Difference of Convex Functions
Provably Efficient Model-based Policy Adaptation
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Reward-Free Exploration for Reinforcement Learning
Rigging the Lottery: Making All Tickets Winners
Robustifying Sequential Neural Processes
Robustness to Programmable String Transformations via Augmented Abstract Training
Scalable Differentiable Physics for Learning and Control
Sequence Generation with Mixed Representations
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
Strategic Classification is Causal Modeling in Disguise
Tails of Lipschitz Triangular Flows
T-GD: Transferable GAN-generated Images Detection Framework
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Transparency Promotion with Model-Agnostic Linear Competitors
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Universal Equivariant Multilayer Perceptrons
Upper bounds for Model-Free Row-Sparse Principal Component Analysis
(ends 3:45 PM)
4 p.m.
Poster Session 42 [4:00-4:45]
Posters 4:00-4:45
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
Better depth-width trade-offs for neural networks through the lens of dynamical systems
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
ConQUR: Mitigating Delusional Bias in Deep Q-Learning
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
Model-Based Reinforcement Learning with Value-Targeted Regression
Multi-Objective Molecule Generation using Interpretable Substructures
On Efficient Constructions of Checkpoints
On Leveraging Pretrained GANs for Generation with Limited Data
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Simultaneous Inference for Massive Data: Distributed Bootstrap
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Stochastic Hamiltonian Gradient Methods for Smooth Games
Time-aware Large Kernel Convolutions
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Adversarial Attacks on Copyright Detection Systems
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Distribution Augmentation for Generative Modeling
Doubly robust off-policy evaluation with shrinkage
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Efficient Continuous Pareto Exploration in Multi-Task Learning
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Generalization via Derandomization
Goal-Aware Prediction: Learning to Model What Matters
Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Graph Optimal Transport for Cross-Domain Alignment
Graph Structure of Neural Networks
Improving generalization by controlling label-noise information in neural network weights
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
Lorentz Group Equivariant Neural Network for Particle Physics
Momentum Improves Normalized SGD
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models
No-Regret and Incentive-Compatible Online Learning
Normalized Loss Functions for Deep Learning with Noisy Labels
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
On hyperparameter tuning in general clustering problemsm
Online Learned Continual Compression with Adaptive Quantization Modules
Online Learning with Imperfect Hints
Optimal Differential Privacy Composition for Exponential Mechanisms
Perceptual Generative Autoencoders
Provable Smoothness Guarantees for Black-Box Variational Inference
Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Semi-Supervised Learning with Normalizing Flows
Semi-Supervised StyleGAN for Disentanglement Learning
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory
Uniform Convergence of Rank-weighted Learning
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Variational Inference for Sequential Data with Future Likelihood Estimates
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
(ends 4:45 PM)
5 p.m.
Poster Session 43 [5:00-5:45]
Posters 5:00-5:45
An Imitation Learning Approach for Cache Replacement
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Coresets for Data-efficient Training of Machine Learning Models
Minimax Pareto Fairness: A Multi Objective Perspective
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
Visual Grounding of Learned Physical Models
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Certified Data Removal from Machine Learning Models
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Cost-Effective Interactive Attention Learning with Neural Attention Processes
Defense Through Diverse Directions
Domain Aggregation Networks for Multi-Source Domain Adaptation
Emergence of Separable Manifolds in Deep Language Representations
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Generalization and Representational Limits of Graph Neural Networks
Generalized and Scalable Optimal Sparse Decision Trees
Gradient Temporal-Difference Learning with Regularized Corrections
High-dimensional Robust Mean Estimation via Gradient Descent
Learning Human Objectives by Evaluating Hypothetical Behavior
Learning Mixtures of Graphs from Epidemic Cascades
Model Fusion with Kullback--Leibler Divergence
Momentum-Based Policy Gradient Methods
One-shot Distributed Ridge Regression in High Dimensions
Optimizing Data Usage via Differentiable Rewards
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning
Progressive Identification of True Labels for Partial-Label Learning
SGD Learns One-Layer Networks in WGANs
Sparse Sinkhorn Attention
The Performance Analysis of Generalized Margin Maximizers on Separable Data
(ends 5:45 PM)
6 p.m.
Poster Session 44 [6:00-6:45]
Posters 6:00-6:45
Bounding the fairness and accuracy of classifiers from population statistics
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Energy-Based Processes for Exchangeable Data
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
On the Global Convergence Rates of Softmax Policy Gradient Methods
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Symbolic Network: Generalized Neural Policies for Relational MDPs
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Accelerated Message Passing for Entropy-Regularized MAP Inference
Active World Model Learning in Agent-rich Environments with Progress Curiosity
A Distributional Framework For Data Valuation
Aligned Cross Entropy for Non-Autoregressive Machine Translation
Bio-Inspired Hashing for Unsupervised Similarity Search
Concept Bottleneck Models
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Evaluating Machine Accuracy on ImageNet
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Generalization Error of Generalized Linear Models in High Dimensions
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Implicit competitive regularization in GANs
Improved Optimistic Algorithms for Logistic Bandits
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Inferring DQN structure for high-dimensional continuous control
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Video Prediction via Example Guidance
WaveFlow: A Compact Flow-based Model for Raw Audio
When Does Self-Supervision Help Graph Convolutional Networks?
(ends 6:45 PM)
7 p.m.
Invited Talk:
Quantum Machine Learning : Prospects and Challenges
Iordanis Kerenidis
(duration 2.0 hr)
9 p.m.
Poster Session 45 [9:00-9:45]
Posters 9:00-9:45
Graph-based Nearest Neighbor Search: From Practice to Theory
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Inter-domain Deep Gaussian Processes
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
Learning Flat Latent Manifolds with VAEs
Missing Data Imputation using Optimal Transport
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Soft Threshold Weight Reparameterization for Learnable Sparsity
A Flexible Latent Space Model for Multilayer Networks
Aggregation of Multiple Knockoffs
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer
Composable Sketches for Functions of Frequencies: Beyond the Worst Case
Conditional gradient methods for stochastically constrained convex minimization
Data-Efficient Image Recognition with Contrastive Predictive Coding
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Double-Loop Unadjusted Langevin Algorithm
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Extra-gradient with player sampling for faster convergence in n-player games
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Gradient-free Online Learning in Continuous Games with Delayed Rewards
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Learning to Rank Learning Curves
Low Bias Low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Monte-Carlo Tree Search as Regularized Policy Optimization
Naive Exploration is Optimal for Online LQR
Near-optimal Regret Bounds for Stochastic Shortest Path
Neural Kernels Without Tangents
Online Continual Learning from Imbalanced Data
Online Multi-Kernel Learning with Graph-Structured Feedback
On Thompson Sampling with Langevin Algorithms
Optimistic Bounds for Multi-output Learning
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
PowerNorm: Rethinking Batch Normalization in Transformers
Predictive Coding for Locally-Linear Control
Random extrapolation for primal-dual coordinate descent
Randomized Block-Diagonal Preconditioning for Parallel Learning
Real-Time Optimisation for Online Learning in Auctions
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Supervised Quantile Normalization for Low Rank Matrix Factorization
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Topologically Densified Distributions
(ends 9:45 PM)
10 p.m.
Poster Session 46 [10:00-10:45]
Posters 10:00-10:45
Amortised Learning by Wake-Sleep
Convolutional Kernel Networks for Graph-Structured Data
Latent Bernoulli Autoencoder
On Efficient Low Distortion Ultrametric Embedding
Preselection Bandits
Stochastic Latent Residual Video Prediction
A Generative Model for Molecular Distance Geometry
A quantile-based approach for hyperparameter transfer learning
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Graph Filtration Learning
Influenza Forecasting Framework based on Gaussian Processes
Kernel interpolation with continuous volume sampling
Learning to Branch for Multi-Task Learning
Modulating Surrogates for Bayesian Optimization
Neural Topic Modeling with Continual Lifelong Learning
Predicting Choice with Set-Dependent Aggregation
Projective Preferential Bayesian Optimization
Robust Learning with the Hilbert-Schmidt Independence Criterion
Spectral Clustering with Graph Neural Networks for Graph Pooling
Stochastic Differential Equations with Variational Wishart Diffusions
Stochastic Subspace Cubic Newton Method
Teaching with Limited Information on the Learner's Behaviour
The Boomerang Sampler
The FAST Algorithm for Submodular Maximization
Topic Modeling via Full Dependence Mixtures
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
(ends 10:45 PM)
11 p.m.
Poster Session 47 [11:00-11:45]
Posters 11:00-11:45
Probing Emergent Semantics in Predictive Agents via Question Answering
The continuous categorical: a novel simplex-valued exponential family
Training Binary Neural Networks using the Bayesian Learning Rule
Anderson Acceleration of Proximal Gradient Methods
Efficiently sampling functions from Gaussian process posteriors
Extrapolation for Large-batch Training in Deep Learning
Forecasting Sequential Data Using Consistent Koopman Autoencoders
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
Linear bandits with Stochastic Delayed Feedback
Multi-Agent Determinantal Q-Learning
Optimistic Policy Optimization with Bandit Feedback
Parallel Algorithm for Non-Monotone DR-Submodular Maximization
Regularized Optimal Transport is Ground Cost Adversarial
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Variational Autoencoders with Riemannian Brownian Motion Priors
(ends 11:45 PM)

FRI 17 JUL
midnight
Poster Session 48 [12:00-12:45]
Posters 12:00-12:45
Deep Streaming Label Learning
Stochastic Optimization for Regularized Wasserstein Estimators
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Learning Factorized Weight Matrix for Joint Filtering
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Off-Policy Actor-Critic with Shared Experience Replay
Randomization matters How to defend against strong adversarial attacks
Reliable Fidelity and Diversity Metrics for Generative Models
Spectral Subsampling MCMC for Stationary Time Series
(ends 12:45 AM)
1 a.m.
Town Hall:
Town Hall Meeting
(duration 1.0 hr)
2 a.m.
Poster Session 49 [2:00-2:45]
Posters 3:00-2:45
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Born-again Tree Ensembles
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Enhancing Simple Models by Exploiting What They Already Know
Error-Bounded Correction of Noisy Labels
FACT: A Diagnostic for Group Fairness Trade-offs
Improving Robustness of Deep-Learning-Based Image Reconstruction
Learning Selection Strategies in Buchberger’s Algorithm
Learning the Valuations of a $k$-demand Agent
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
New Oracle-Efficient Algorithms for Private Synthetic Data Release
p-Norm Flow Diffusion for Local Graph Clustering
Privately detecting changes in unknown distributions
Robust Pricing in Dynamic Mechanism Design
Stochastic Flows and Geometric Optimization on the Orthogonal Group
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
Almost Tune-Free Variance Reduction
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
BINOCULARS for efficient, nonmyopic sequential experimental design
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Calibration, Entropy Rates, and Memory in Language Models
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Concise Explanations of Neural Networks using Adversarial Training
Constrained Markov Decision Processes via Backward Value Functions
Continuous Graph Neural Networks
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Estimation of Bounds on Potential Outcomes For Decision Making
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Graph Homomorphism Convolution
GraphOpt: Learning Optimization Models of Graph Formation
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Interpreting Robust Optimization via Adversarial Influence Functions
Invariant Rationalization
Latent Variable Modelling with Hyperbolic Normalizing Flows
Learning Robot Skills with Temporal Variational Inference
Learning with Bounded Instance- and Label-dependent Label Noise
Low-Rank Bottleneck in Multi-head Attention Models
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Multi-Agent Routing Value Iteration Network
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Online Learning with Dependent Stochastic Feedback Graphs
On the Expressivity of Neural Networks for Deep Reinforcement Learning
Optimizing Dynamic Structures with Bayesian Generative Search
Piecewise Linear Regression via a Difference of Convex Functions
Reward-Free Exploration for Reinforcement Learning
Rigging the Lottery: Making All Tickets Winners
Scalable Differentiable Physics for Learning and Control
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Strategic Classification is Causal Modeling in Disguise
Transparency Promotion with Model-Agnostic Linear Competitors
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Universal Equivariant Multilayer Perceptrons
Upper bounds for Model-Free Row-Sparse Principal Component Analysis
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
Representation Learning via Adversarially-Contrastive Optimal Transport
A Graph to Graphs Framework for Retrosynthesis Prediction
A Tree-Structured Decoder for Image-to-Markup Generation
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Do We Need Zero Training Loss After Achieving Zero Training Error?
DropNet: Reducing Neural Network Complexity via Iterative Pruning
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Learning Autoencoders with Relational Regularization
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
More Information Supervised Probabilistic Deep Face Embedding Learning
Multinomial Logit Bandit with Low Switching Cost
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Non-autoregressive Machine Translation with Disentangled Context Transformer
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
On the Power of Compressed Sensing with Generative Models
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Self-Attentive Associative Memory
Sparse Subspace Clustering with Entropy-Norm
Transformer Hawkes Process
(ends 2:45 AM)
3 a.m.
Poster Session 50 [3:00-3:45]
Posters 3:00-3:45
Acceleration through spectral density estimation
Adaptive Adversarial Multi-task Representation Learning
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
Differentiable Product Quantization for End-to-End Embedding Compression
Do GANs always have Nash equilibria?
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Improving Transformer Optimization Through Better Initialization
On a projective ensemble approach to two sample test for equality of distributions
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Task Understanding from Confusing Multi-task Data
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Adaptive Region-Based Active Learning
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Bandits for BMO Functions
Black-Box Methods for Restoring Monotonicity
Bridging the Gap Between f-GANs and Wasserstein GANs
Causal Inference using Gaussian Processes with Structured Latent Confounders
Convex Calibrated Surrogates for the Multi-Label F-Measure
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Countering Language Drift with Seeded Iterated Learning
Deep Reinforcement Learning with Smooth Policy
Does label smoothing mitigate label noise?
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Input-Sparsity Low Rank Approximation in Schatten Norm
Linear Lower Bounds and Conditioning of Differentiable Games
Mapping natural-language problems to formal-language solutions using structured neural representations
Meta Variance Transfer: Learning to Augment from the Others
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Robustifying Sequential Neural Processes
Robustness to Programmable String Transformations via Augmented Abstract Training
Sequence Generation with Mixed Representations
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
ConQUR: Mitigating Delusional Bias in Deep Q-Learning
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Multi-Objective Molecule Generation using Interpretable Substructures
On Efficient Constructions of Checkpoints
On Leveraging Pretrained GANs for Generation with Limited Data
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Simultaneous Inference for Massive Data: Distributed Bootstrap
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Stochastic Hamiltonian Gradient Methods for Smooth Games
Time-aware Large Kernel Convolutions
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Adversarial Attacks on Copyright Detection Systems
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Efficient Continuous Pareto Exploration in Multi-Task Learning
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Generalization via Derandomization
Goal-Aware Prediction: Learning to Model What Matters
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Graph Optimal Transport for Cross-Domain Alignment
Improving generalization by controlling label-noise information in neural network weights
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
Momentum Improves Normalized SGD
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models
No-Regret and Incentive-Compatible Online Learning
On hyperparameter tuning in general clustering problemsm
Online Learned Continual Compression with Adaptive Quantization Modules
Online Learning with Imperfect Hints
Provable Smoothness Guarantees for Black-Box Variational Inference
Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Uniform Convergence of Rank-weighted Learning
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Few-shot Domain Adaptation by Causal Mechanism Transfer
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
On Layer Normalization in the Transformer Architecture
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
(ends 3:45 AM)
4 a.m.
Poster Session 51 [4:00-4:45]
Posters 3:00-5:45
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Reducing Sampling Error in Batch Temporal Difference Learning
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching
Universal Asymptotic Optimality of Polyak Momentum
Do RNN and LSTM have Long Memory?
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Learning Optimal Tree Models under Beam Search
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems
On Breaking Deep Generative Model-based Defenses and Beyond
Performative Prediction
Provably Efficient Model-based Policy Adaptation
Tails of Lipschitz Triangular Flows
T-GD: Transferable GAN-generated Images Detection Framework
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
Model-Based Reinforcement Learning with Value-Targeted Regression
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Distribution Augmentation for Generative Modeling
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
An Imitation Learning Approach for Cache Replacement
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Coresets for Data-efficient Training of Machine Learning Models
Minimax Pareto Fairness: A Multi Objective Perspective
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
Visual Grounding of Learned Physical Models
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Certified Data Removal from Machine Learning Models
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Cost-Effective Interactive Attention Learning with Neural Attention Processes
Defense Through Diverse Directions
Domain Aggregation Networks for Multi-Source Domain Adaptation
Emergence of Separable Manifolds in Deep Language Representations
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Generalization and Representational Limits of Graph Neural Networks
Generalized and Scalable Optimal Sparse Decision Trees
Gradient Temporal-Difference Learning with Regularized Corrections
High-dimensional Robust Mean Estimation via Gradient Descent
Learning Human Objectives by Evaluating Hypothetical Behavior
Learning Mixtures of Graphs from Epidemic Cascades
Momentum-Based Policy Gradient Methods
One-shot Distributed Ridge Regression in High Dimensions
Optimizing Data Usage via Differentiable Rewards
Progressive Identification of True Labels for Partial-Label Learning
SGD Learns One-Layer Networks in WGANs
Sparse Sinkhorn Attention
The Performance Analysis of Generalized Margin Maximizers on Separable Data
(ends 4:45 AM)
5 a.m.
Poster Session 52 [5:00-5:45]
Posters 4:00-6:45
Better depth-width trade-offs for neural networks through the lens of dynamical systems
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Doubly robust off-policy evaluation with shrinkage
Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Graph Structure of Neural Networks
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Lorentz Group Equivariant Neural Network for Particle Physics
Normalized Loss Functions for Deep Learning with Noisy Labels
Optimal Differential Privacy Composition for Exponential Mechanisms
Perceptual Generative Autoencoders
Semi-Supervised Learning with Normalizing Flows
Semi-Supervised StyleGAN for Disentanglement Learning
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory
Variational Inference for Sequential Data with Future Likelihood Estimates
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
Model Fusion with Kullback--Leibler Divergence
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
On the Global Convergence Rates of Softmax Policy Gradient Methods
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Accelerated Message Passing for Entropy-Regularized MAP Inference
Aligned Cross Entropy for Non-Autoregressive Machine Translation
Bio-Inspired Hashing for Unsupervised Similarity Search
Concept Bottleneck Models
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Evaluating Machine Accuracy on ImageNet
Generalization Error of Generalized Linear Models in High Dimensions
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Implicit competitive regularization in GANs
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Inferring DQN structure for high-dimensional continuous control
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Video Prediction via Example Guidance
When Does Self-Supervision Help Graph Convolutional Networks?
(ends 5:45 AM)
8 a.m.
Invited Talk:
Quantum Machine Learning : Prospects and Challenges
Iordanis Kerenidis
(duration 2.0 hr)
Poster Session 53 [8:00-8:45]
Posters 6:00-9:45
Bounding the fairness and accuracy of classifiers from population statistics
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Energy-Based Processes for Exchangeable Data
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Active World Model Learning in Agent-rich Environments with Progress Curiosity
A Distributional Framework For Data Valuation
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Improved Optimistic Algorithms for Logistic Bandits
WaveFlow: A Compact Flow-based Model for Raw Audio
Inter-domain Deep Gaussian Processes
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
Learning Flat Latent Manifolds with VAEs
Missing Data Imputation using Optimal Transport
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
A Flexible Latent Space Model for Multilayer Networks
Composable Sketches for Functions of Frequencies: Beyond the Worst Case
Naive Exploration is Optimal for Online LQR
Online Continual Learning from Imbalanced Data
Online Multi-Kernel Learning with Graph-Structured Feedback
On Thompson Sampling with Langevin Algorithms
Topologically Densified Distributions
(ends 8:45 AM)
9 a.m.
Poster Session 54 [9:00-9:45]
Posters 6:00-10:45
Symbolic Network: Generalized Neural Policies for Relational MDPs
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Soft Threshold Weight Reparameterization for Learnable Sparsity
Double-Loop Unadjusted Langevin Algorithm
Gradient-free Online Learning in Continuous Games with Delayed Rewards
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Low Bias Low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks
Neural Kernels Without Tangents
PowerNorm: Rethinking Batch Normalization in Transformers
Random extrapolation for primal-dual coordinate descent
Randomized Block-Diagonal Preconditioning for Parallel Learning
Real-Time Optimisation for Online Learning in Auctions
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Learning to Branch for Multi-Task Learning
Robust Learning with the Hilbert-Schmidt Independence Criterion
The Boomerang Sampler
Topic Modeling via Full Dependence Mixtures
(ends 9:45 AM)
10 a.m.
Poster Session 55 [10:00-10:45]
Posters 9:00-11:45
Graph-based Nearest Neighbor Search: From Practice to Theory
Aggregation of Multiple Knockoffs
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer
Conditional gradient methods for stochastically constrained convex minimization
Data-Efficient Image Recognition with Contrastive Predictive Coding
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Extra-gradient with player sampling for faster convergence in n-player games
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Learning to Rank Learning Curves
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Monte-Carlo Tree Search as Regularized Policy Optimization
Near-optimal Regret Bounds for Stochastic Shortest Path
Optimistic Bounds for Multi-output Learning
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Predictive Coding for Locally-Linear Control
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Supervised Quantile Normalization for Low Rank Matrix Factorization
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Stochastic Latent Residual Video Prediction
A quantile-based approach for hyperparameter transfer learning
Graph Filtration Learning
Neural Topic Modeling with Continual Lifelong Learning
Predicting Choice with Set-Dependent Aggregation
Spectral Clustering with Graph Neural Networks for Graph Pooling
Stochastic Differential Equations with Variational Wishart Diffusions
Stochastic Subspace Cubic Newton Method
Anderson Acceleration of Proximal Gradient Methods
Extrapolation for Large-batch Training in Deep Learning
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase
(ends 10:45 AM)
11 a.m.
Workshops Fri [11:00-5:00]
Workshops 9:05-3:00
Self-supervision in Audio and Speech
5th ICML Workshop on Human Interpretability in Machine Learning (WHI)
Law & Machine Learning
Learning with Missing Values
Healthcare Systems, Population Health, and the Role of Health-tech
Challenges in Deploying and Monitoring Machine Learning Systems
Workshop on AI for Autonomous Driving (AIAD)
MLRetrospectives: A Venue for Self-Reflection in ML Research
ICML 2020 Workshop on Computational Biology
Participatory Approaches to Machine Learning
Workshop on Continual Learning
Workshop on eXtreme Classification: Theory and Applications
Object-Oriented Learning: Perception, Representation, and Reasoning
Theoretical Foundations of Reinforcement Learning
ML Interpretability for Scientific Discovery
Uncertainty and Robustness in Deep Learning Workshop (UDL)
Beyond first order methods in machine learning systems
(ends 5:00 AM)
Poster Session 56 [11:00-11:45]
Posters 10:00-12:45
Amortised Learning by Wake-Sleep
Convolutional Kernel Networks for Graph-Structured Data
Latent Bernoulli Autoencoder
On Efficient Low Distortion Ultrametric Embedding
Preselection Bandits
A Generative Model for Molecular Distance Geometry
Influenza Forecasting Framework based on Gaussian Processes
Kernel interpolation with continuous volume sampling
Modulating Surrogates for Bayesian Optimization
Projective Preferential Bayesian Optimization
Teaching with Limited Information on the Learner's Behaviour
The FAST Algorithm for Submodular Maximization
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
Training Binary Neural Networks using the Bayesian Learning Rule
Optimistic Policy Optimization with Bandit Feedback
Stochastic Optimization for Regularized Wasserstein Estimators
Reliable Fidelity and Diversity Metrics for Generative Models
(ends 11:45 AM)
noon
Poster Session 57 [12:00-12:45]
Posters 11:00-12:45
Probing Emergent Semantics in Predictive Agents via Question Answering
The continuous categorical: a novel simplex-valued exponential family
Efficiently sampling functions from Gaussian process posteriors
Forecasting Sequential Data Using Consistent Koopman Autoencoders
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Linear bandits with Stochastic Delayed Feedback
Multi-Agent Determinantal Q-Learning
Parallel Algorithm for Non-Monotone DR-Submodular Maximization
Regularized Optimal Transport is Ground Cost Adversarial
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Variational Autoencoders with Riemannian Brownian Motion Priors
Spectral Subsampling MCMC for Stationary Time Series
(ends 12:45 PM)
1 p.m.
Poster Session 58 [1:00-1:45]
Posters 12:00-3:45
Deep Streaming Label Learning
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Learning Factorized Weight Matrix for Joint Filtering
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Off-Policy Actor-Critic with Shared Experience Replay
Randomization matters How to defend against strong adversarial attacks
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
Representation Learning via Adversarially-Contrastive Optimal Transport
A Graph to Graphs Framework for Retrosynthesis Prediction
A Tree-Structured Decoder for Image-to-Markup Generation
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Do We Need Zero Training Loss After Achieving Zero Training Error?
DropNet: Reducing Neural Network Complexity via Iterative Pruning
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Learning Autoencoders with Relational Regularization
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
More Information Supervised Probabilistic Deep Face Embedding Learning
Multinomial Logit Bandit with Low Switching Cost
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Non-autoregressive Machine Translation with Disentangled Context Transformer
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
On the Power of Compressed Sensing with Generative Models
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Self-Attentive Associative Memory
Sparse Subspace Clustering with Entropy-Norm
Transformer Hawkes Process
Few-shot Domain Adaptation by Causal Mechanism Transfer
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
On Layer Normalization in the Transformer Architecture
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
(ends 1:45 PM)

SAT 18 JUL
9 a.m.
10 a.m.
Workshops Sat [10:00-5:00]
Workshops 11:00-12:00
4th Lifelong Learning Workshop
INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models
Inductive Biases, Invariances and Generalization in Reinforcement Learning
Negative Dependence and Submodularity: Theory and Applications in Machine Learning
Federated Learning for User Privacy and Data Confidentiality
Machine Learning for Global Health
Bridge Between Perception and Reasoning: Graph Neural Networks & Beyond
7th ICML Workshop on Automated Machine Learning (AutoML 2020)
Economics of privacy and data labor
1st Workshop on Language in Reinforcement Learning (LaReL)
Real World Experiment Design and Active Learning
Workshop on Learning in Artificial Open Worlds
Incentives in Machine Learning
Machine Learning for Media Discovery
2nd ICML Workshop on Human in the Loop Learning (HILL)
(ends 5:00 AM)