Workshop
Sampling and Optimization in Discrete Space
Haoran Sun · Hanjun Dai · Priyank Jaini · Ruqi Zhang · Ellen Vitercik
Sat 29 Jul, noon PDT
There have recently been new research trends in efficient discrete sampling and optimization. We are organizing this workshop with the goals of 1) syncing up on the latest research progress in discrete sampling and optimization, 2) discussing the limitations of current methods and brainstorming new algorithm paradigms, and 3) connecting to applications in domains such as language/protein modeling, physics simulation, and bio/chemical engineering---where improved techniques for sampling/optimization in discrete space could help---and exploring the gaps between the application's needs and the capabilities of existing methods. We hope this workshop will be an excellent opportunity for presenting and discussing new algorithms and applications with researchers and practitioners within or outside the domain of discrete sampling/optimization.
Schedule
Sat 12:00 p.m. - 12:15 p.m.
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Opening Remarks
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Opening Remarks
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Sat 12:15 p.m. - 12:45 p.m.
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Yoshua Bengio: GFlowNets for Bayesian Inference
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Invited Talk
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Sat 12:45 p.m. - 1:15 p.m.
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Giacomo Zanella
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Invited Talk
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Sat 1:15 p.m. - 1:45 p.m.
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Contributed Talk 1
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Contributed Talk
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Sat 1:45 p.m. - 2:15 p.m.
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Stefanie Jegelka: Learning discrete optimization: Loss functions and graph neural networks
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Invited Talk
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Sat 2:15 p.m. - 2:30 p.m.
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Coffee Break
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Sat 2:30 p.m. - 4:00 p.m.
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Poster Session 1
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Poster Session
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Sat 4:00 p.m. - 4:30 p.m.
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Will Grathwohl: Recent Applications of Gradients in Discrete Sampling
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Invited Talk
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Sat 4:30 p.m. - 5:00 p.m.
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Lianhui Qin: Differentiable and structured text reasoning
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Invited Talk
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SlidesLive Video |
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Sat 5:00 p.m. - 5:30 p.m.
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Contributed Talk 2
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Contributed Talk
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SlidesLive Video |
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Sat 5:30 p.m. - 6:00 p.m.
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Petar Veličković: The Melting Pot of Neural Algorithmic Reasoning
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Invited Talk
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SlidesLive Video |
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Sat 6:00 p.m. - 6:15 p.m.
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Closing Remarks
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Closing Remark
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SlidesLive Video |
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Sat 6:15 p.m. - 7:45 p.m.
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Poster Session 2
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Poster Session
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SlidesLive Video |
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Differentiable Search of Evolutionary Trees
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Poster
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SlidesLive Video |
Ramith Hettiarachchi · Sergey Ovchinnikov 🔗 |
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Diffusion on the Probability Simplex ( Poster ) > link | Griffin Floto · Thorsteinn Jonsson · Mihai Nica · Scott Sanner · Eric Zhu 🔗 |
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DISCS: A Benchmark for Discrete Sampling ( Poster ) > link | Katayoon Goshvadi · Haoran Sun · Xingchao Liu · Azade Nova · Ruqi Zhang · Will Grathwohl · Dale Schuurmans · Hanjun Dai 🔗 |
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Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
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Oral
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SlidesLive Video |
Alexander Lew · Tan Zhi-Xuan · Gabriel Grand · Vikash Mansinghka 🔗 |
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Constrained Sampling of Discrete Geometric Manifolds using Denoising Diffusion Probabilistic Models ( Poster ) > link | Justin Diamond · Markus Lill 🔗 |
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Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation
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Oral
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SlidesLive Video |
Junwei Huang · Zhiqing Sun · Yiming Yang 🔗 |
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SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems ( Oral ) > link | Aaron Ferber · Taoan Huang · Daochen Zha · Martin Schubert · Benoit Steiner · Bistra Dilkina · Yuandong Tian 🔗 |
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Can LLMs Generate Random Numbers? Evaluating LLM Sampling in Controlled Domains ( Poster ) > link | Aspen Hopkins · Alex Renda · Michael Carbin 🔗 |
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Protein Design with Guided Discrete Diffusion
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Poster
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SlidesLive Video |
Nate Gruver · Samuel Stanton · Nathan Frey · Tim G. J. Rudner · Isidro Hotzel · Julien Lafrance-Vanasse · Arvind Rajpal · Kyunghyun Cho · Andrew Wilson 🔗 |
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Efficient Location Sampling Algorithms for Road Networks ( Poster ) > link | Sara Ahmadian · Kostas Kollias · Ameya Velingker · Sreenivas Gollapudi · Vivek Kumar · Santhoshini Velusamy 🔗 |
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Towards Accelerating Benders Decomposition via Reinforcement Learning Surrogate Models
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Poster
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SlidesLive Video |
Stephen Mak · Kyle Mana · Parisa Zehtabi · Michael Cashmore · Daniele Magazzeni · Manuela Veloso 🔗 |
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Understanding prompt engineering does not require rethinking generalization
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Oral
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SlidesLive Video |
Victor Akinwande · Yiding Jiang · Dylan Sam · Zico Kolter 🔗 |
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GFlowNets for Causal Discovery: an Overview
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Poster
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SlidesLive Video |
Dragos Cristian Manta · Edward Hu · Yoshua Bengio 🔗 |
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Topological Neural Discrete Representation Learning à la Kohonen
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Oral
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SlidesLive Video |
Kazuki Irie · Robert Cordas · Jürgen Schmidhuber 🔗 |
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Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
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Poster
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SlidesLive Video |
Arman Zharmagambetov · Brandon Amos · Aaron Ferber · Taoan Huang · Bistra Dilkina · Yuandong Tian 🔗 |
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Efficient data selection employing Semantic Similarity-based Graph Structures for model training ( Poster ) > link | Roxana Petcu · Subhadeep Maji 🔗 |
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Tackling Provably Hard Representative Selection viaGraph Neural Networks ( Oral ) > link | Mehran Kazemi · Anton Tsitsulin · Hossein Esfandiari · MohammadHossein Bateni · Deepak Ramachandran · Bryan Perozzi · Vahab Mirrokni 🔗 |
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Training Discrete EBMs with Energy Discrepancy ( Poster ) > link | Tobias Schröder · Zijing Ou · Yingzhen Li · Andrew Duncan 🔗 |
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Finite-state Offline Reinforcement Learning with Moment-based Bayesian Epistemic and Aleatoric Uncertainties ( Poster ) > link | Filippo Valdettaro · Aldo Faisal 🔗 |
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Annealed Biological Sequence Optimization ( Poster ) > link | Yuxuan Song · Botian Wang · Hao Zhou · Wei-Ying Ma 🔗 |
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Hierarchical Decomposition Framework for Feasibility-hard Combinatorial Optimization ( Poster ) > link |
12 presentersHanbum Ko · Minu Kim · Han-Seul Jeong · Sunghoon Hong · Deunsol Yoon · Youngjoon Park · Woohyung Lim · Honglak Lee · Moontae Lee · Kanghoon Lee · Sungbin Lim · Sungryull Sohn |
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Global Optimality in Bivariate Gradient-based DAG Learning ( Poster ) > link | Chang Deng · Kevin Bello · Pradeep Ravikumar · Bryon Aragam 🔗 |
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Strictly Low Rank Constraint Optimization \\ --- An Asymptotically $\mathcal{O}(\frac{1}{t^2})$ Method ( Poster ) > link | Mengyuan Zhang · Kai Liu 🔗 |
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Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
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Poster
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SlidesLive Video |
Taoan Huang · Aaron Ferber · Yuandong Tian · Bistra Dilkina · Benoit Steiner 🔗 |
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Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences ( Poster ) > link | Zhishen Huang 🔗 |
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Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion ( Poster ) > link | Alex Tseng · Nathaniel Diamant · Tommaso Biancalani · Gabriele Scalia 🔗 |
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Sequential Attention for Feature Selection ( Poster ) > link | Taisuke Yasuda · MohammadHossein Bateni · Lin Chen · Matthew Fahrbach · Thomas Fu · Vahab Mirrokni 🔗 |
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Tensor Proxies for Efficient Feature Cross Search ( Poster ) > link | Taisuke Yasuda · MohammadHossein Bateni · Lin Chen · Matthew Fahrbach · Thomas Fu 🔗 |
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Guided Evolution with Binary Predictors for ML Program Search ( Poster ) > link | John Co-Reyes · Yingjie Miao · George Tucker · Aleksandra Faust · Esteban Real 🔗 |
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Discrete Diffusion Reward Guidance Methods for Offline Reinforcement Learning ( Poster ) > link | Matthew Coleman · Olga Russakovsky · Christine Allen-Blanchette · Ye Zhu 🔗 |
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Categorical SDEs with Simplex Diffusion ( Poster ) > link | Pierre Richemond · Sander Dieleman · Arnaud Doucet 🔗 |
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SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces ( Poster ) > link | Masaki Adachi · Satoshi Hayakawa · Saad Hamid · Martin Jørgensen · Harald Oberhauser · Michael A Osborne 🔗 |
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Solving NP-hard Min-max Routing Problems as Sequential Generation with Equity Context ( Poster ) > link | Jiwoo Son · Minsu Kim · Sanghyeok Choi · Hyeonah Kim · Jinkyoo Park 🔗 |
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Optimizing protein fitness using Gibbs sampling with Graph-based Smoothing ( Poster ) > link | Andrew Kirjner · Jason Yim · Raman Samusevich · Tommi Jaakkola · Regina Barzilay · Ila R. Fiete 🔗 |
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Symmetric Exploration in Combinatorial Optimization is Free! ( Poster ) > link | Hyeonah Kim · Minsu Kim · Sungsoo Ahn · Jinkyoo Park 🔗 |
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An Optimal Clustering Algorithm for the Labeled Stochastic Block Model
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Poster
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SlidesLive Video |
Kaito Ariu · Se-Young Yun · Alexandre Proutiere 🔗 |