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Recent years have witnessed the rising need for the machine learning systems that can interact with humans in the learning loop. Such systems can be applied to computer vision, natural language processing, robotics, and human computer interaction. Creating and running such systems call for interdisciplinary research of artificial intelligence, machine learning, and software engineering design, which we abstract as Human in the Loop Learning (HILL). The HILL workshop aims to bring together researchers and practitioners working on the broad areas of HILL, ranging from the interactive/active learning algorithms for real-world decision making systems (e.g., autonomous driving vehicles, robotic systems, etc.), lifelong learning systems that retain knowledge from different tasks and selectively transfer knowledge to learn new tasks over a lifetime, models with strong explainability, as well as interactive system designs (e.g., data visualization, annotation systems, etc.). The HILL workshop continues the previous effort to provide a platform for researchers from interdisciplinary areas to share their recent research. In this year’s workshop, a special feature is to encourage the debate between HILL and label-efficient learning: Are these two learning paradigms contradictory with each other, or can they be organically combined to create a more powerful learning system? We believe the theme of the workshop will be of interest for broad ICML attendees, especially those who are interested in interdisciplinary study.

Adversarial machine learning is a new gamut of technologies that aim to study the vulnerabilities of ML approaches and detect malicious behaviors in adversarial settings. The adversarial agents can deceive an ML classifier by significantly altering its response with imperceptible perturbations to the inputs. Although it is not to be alarmist, researchers in machine learning are responsible for preempting attacks and building safeguards, especially when the task is critical for information security and human lives. We need to deepen our understanding of machine learning in adversarial environments.

While the negative implications of this nascent technology have been widely discussed, researchers in machine learning are yet to explore their positive opportunities in numerous aspects. The positive impacts of adversarial machine learning are not limited to boost the robustness of ML models but cut across several other domains.

Since there are both positive and negative applications of adversarial machine learning, tackling adversarial learning to its use in the right direction requires a framework to embrace the positives. This workshop aims to bring together researchers and practitioners from various communities (e.g., machine learning, computer security, data privacy, and ethics) to synthesize promising ideas and research directions and foster and strengthen cross-community collaborations on both theoretical studies and practical applications. Different from the previous workshops on adversarial machine learning, our proposed workshop seeks to explore the prospects besides reducing the unintended risks for sophisticated ML models.

This is a one-day workshop, planned with a 10-minute opening, 11 invited keynotes, about 9 contributed talks, 2 poster sessions, and 2 special sessions for panel discussion about the prospects and perils of adversarial machine learning.

The workshop is kindly sponsored by RealAI Inc. and Bosch.

Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this workshop, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. Specifically, we plan to facilitate workshop interactions that will shed light onto the following 3 questions: (i) What are the practical, legal and ethical considerations that decision-makers need to account for when providing recourse? (ii) How do humans understand and act based on recourse explanations from a psychological and behavioral perspective? (iii) What are the main technical advances in explainability and causality in ML required for achieving recourse? Our ultimate goal is to foster conversations that will help bridge the gaps arising from the interdisciplinary nature of algorithmic recourse and contribute towards the wider adoption of such methods.

Training machine learning models in a centralized fashion often faces significant challenges due to regulatory and privacy concerns in real-world use cases. These include distributed training data, computational resources to create and maintain a central data repository, and regulatory guidelines (GDPR, HIPAA) that restrict sharing sensitive data. Federated learning (FL) is a new paradigm in machine learning that can mitigate these challenges by training a global model using distributed data, without the need for data sharing. The extensive application of machine learning to analyze and draw insight from real-world, distributed, and sensitive data necessitates familiarization with and adoption of this relevant and timely topic among the scientific community.

Despite the advantages of FL, and its successful application in certain industry-based cases, this field is still in its infancy due to new challenges that are imposed by limited visibility of the training data, potential lack of trust among participants training a single model, potential privacy inferences, and in some cases, limited or unreliable connectivity.

The goal of this workshop is to bring together researchers and practitioners interested in FL. This day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to understand the topic, identify technical challenges, and discuss potential solutions. This will lead to an overall advancement of FL and its impact in the community, while noting that FL has become an increasingly popular topic in the ICML community in recent years.

Machine learning (ML) systems have been increasingly used in many applications, ranging from decision-making systems to safety-critical tasks. While the hope is to improve decision-making accuracy and societal outcomes with these ML models, concerns have been incurred that they can inflict harm if not developed or used with care. It has been well-documented that ML models can: (1) inherit pre-existing biases and exhibit discrimination against already-disadvantaged or marginalized social groups; (2) be vulnerable to security and privacy attacks that deceive the models and leak the training data's sensitive information; (3) make hard-to-justify predictions with a lack of transparency. Therefore, it is essential to build socially responsible ML models that are fair, robust, private, transparent, and interpretable.

Although extensive studies have been conducted to increase trust in ML, many of them either focus on well-defined problems that enable nice tractability from a mathematical perspective but are hard to adapt to real-world systems, or they mainly focus on mitigating risks in real-world applications without providing theoretical justifications. Moreover, most work studies those issues separately; the connections among them are less well-understood. This workshop aims to build connections by bringing together both theoretical and applied researchers from various communities (e.g., machine learning, fairness & ethics, security, privacy, etc.). We aim to synthesize promising ideas and research directions, as well as strengthen cross-community collaborations. We hope to chart out important directions for future work. We have an advisory committee and confirmed speakers whose expertise represents the diversity of the technical problems in this emerging research field.

The ICML Workshop on Computational Biology will highlight how machine learning approaches can be tailored to making discoveries with biological data. Practitioners at the intersection of computation, machine learning, and biology are in a unique position to frame problems in biomedicine, from drug discovery to vaccination risk scores, and the Workshop will showcase such recent research. Commodity lab techniques lead to the proliferation of large complex datasets, and require new methods to interpret these collections of high-dimensional biological data, such as genetic sequences, cellular features or protein structures, and imaging datasets. These data can be used to make new predictions towards clinical response, to uncover new biology, or to aid in drug discovery.
This workshop aims to bring together interdisciplinary machine learning researchers working at the intersection of machine learning and biology that includes areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community.
The workshop is a sequel to the WCB workshops we organized in the last five years at ICML, which had excellent line-ups of talks and were well-received by the community. Every year, we received 60+ submissions. After multiple rounds of rigorous reviewing, around 50 submissions were selected from which the best set of papers were chosen for Contributed talks and Spotlights and the rest were invited for Poster presentations. We have a steadfast and growing base of reviewers making up the Program Committee. For two of the previous editions, a special issue of Journal of Computational Biology has been released with extended versions of a selected set of accepted papers.

A growing number of machine learning problems involve finding subsets of data points. Examples range from selecting subset of labeled or unlabeled data points, to subsets of features or model parameters, to selecting subsets of pixels, keypoints, sentences etc. in image segmentation, correspondence and summarization problems. The workshop would encompass a wide variety of topics ranging from theoretical aspects of subset selection e.g. coresets, submodularity, determinantal point processes, to several practical applications, {\em e.g.}, time and energy efficient learning, learning under resource constraints, active learning, human assisted learning, feature selection, model compression, feature induction, {\em etc.}

We believe that this workshop is very timely since, a) subset selection is naturally emerging and has often been considered in isolation in many of the above applications, and b) by connecting researchers working on both the theoretical and application domains above, we can foster a much needed discussion on reusing a several technical innovations across these subareas and applications. Furthermore, we would also like to connect researchers working on the theoretical foundations of subset selection (in areas such as coresets and submodularity) with researchers working in applications (such as feature selection, active learning, data efficient learning, model compression, and human assisted machine learning).

The rising prevalence of mental illness has posed a growing global burden, with one in four people adversely affected at some point in their lives, accounting for 32.4% of years lived with disability. This has only been exacerbated during the current pandemic, and while the capacity of acute care has been significantly increased in response to the crisis, it has at the same time led to the scaling back of many mental health services. This, together with the advances in the field of machine learning (ML), has motivated exploration of how machine learning methods can be applied to the provision of more effective and efficient mental healthcare, from varied approaches to continual monitoring of individual mental health or identification of mental health issues through inferences about behaviours on social media, online searches or mobile apps, to predictive models for early diagnosis and intervention, understanding disease progression or recovery, and the personalization of therapies.

This workshop aims to bring together clinicians, behavioural scientists and machine learning researchers working in various facets of mental health and care provision, to identify the key opportunities and challenges in developing solutions for this domain, and discussing the progress made.

Optimization lies at the heart of many exciting developments in machine learning, statistics and signal processing. As models become more complex and datasets get larger, finding efficient, reliable and provable methods is one of the primary goals in these fields.

In the last few decades, much effort has been devoted to the development of first-order methods. These methods enjoy a low per-iteration cost and have optimal complexity, are easy to implement, and have proven to be effective for most machine learning applications. First-order methods, however, have significant limitations: (1) they require fine hyper-parameter tuning, (2) they do not incorporate curvature information, and thus are sensitive to ill-conditioning, and (3) they are often unable to fully exploit the power of distributed computing architectures.

Higher-order methods, such as Newton, quasi-Newton and adaptive gradient descent methods, are extensively used in many scientific and engineering domains. At least in theory, these methods possess several nice features: they exploit local curvature information to mitigate the effects of ill-conditioning, they avoid or diminish the need for hyper-parameter tuning, and they have enough concurrency to take advantage of distributed computing environments. Researchers have even developed stochastic versions of higher-order methods, that feature speed and scalability by incorporating curvature information in an economical and judicious manner. However, often higher-order methods are “undervalued.”

This workshop will attempt to shed light on this statement. Topics of interest include, but are not limited to, second-order methods, adaptive gradient descent methods, regularization techniques, as well as techniques based on higher-order derivatives.

The empirical success of state-of-the-art machine learning (ML) techniques has outpaced their theoretical understanding. Deep learning models, for example, perform far better than classical statistical learning theory predicts, leading to its widespread use by Industry and Government. At the same time, the deployment of ML systems that are not fully understood often leads to unexpected and detrimental individual-level impact. Finally, the large-scale adoption of ML means that ML systems are now critical infrastructure on which millions rely. In the face of these challenges, there is a critical need for theory that provides rigorous performance guarantees for practical ML models; guides the responsible deployment of ML in applications of social consequence; and enables the design of reliable ML systems in large-scale, distributed environments.

For decades, information theory has provided a mathematical foundation for the systems and algorithms that fuel the current data science revolution. Recent advances in privacy, fairness, and generalization bounds demonstrate that information theory will also play a pivotal role in the next decade of ML applications: information-theoretic methods can sharpen generalization bounds for deep learning, provide rigorous guarantees for compression of neural networks, promote fairness and privacy in ML training and deployment, and shed light on the limits of learning from noisy data.

We propose a workshop that brings together researchers and practitioners in ML and information theory to encourage knowledge transfer and collaboration between the sister fields. For information theorists, the workshop will highlight novel and socially-critical research directions that promote reliable, responsible, and rigorous development of ML. Moreover, the workshop will expose ICML attendees to emerging information-theoretic tools that may play a critical role in the next decade of ML applications.

Visit https://sites.google.com/berkeley.edu/dfuq21/ for details!

While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying learning systems in consequential settings also requires calibrating and communicating the uncertainty of predictions. A recent line of work we call distribution-free predictive inference (i.e., conformal prediction and related methods) has developed a set of methods that give finite-sample statistical guarantees for any (possibly incorrectly specified) predictive model and any (unknown) underlying distribution of the data, ensuring reliable uncertainty quantification (UQ) for many prediction tasks. This line of work represents a promising new approach to UQ with complex prediction systems but is relatively unknown in the applied machine learning community. Moreover, much remains to be done integrating distribution-free methods with existing approaches to UQ via calibration (e.g. with temperature scaling) -- little work has been done to bridge these two worlds. To facilitate the emerging topics on distribution-free methods, the proposed workshop has two goals. First, to bring together researchers in distribution-free methods with researchers specializing in calibration techniques to catalyze work at this interface. Second, to introduce distribution-free methods to a wider ML audience. Given the important recent emphasis on the reliable real-world performance of ML models, we believe a large fraction of ICML attendees will find this workshop highly relevant.

Self-supervised learning (SSL) is an unsupervised approach for representation learning without relying on human-provided labels. It creates auxiliary tasks on unlabeled input data and learns representations by solving these tasks. SSL has demonstrated great success on images, texts, robotics, etc. On a wide variety of tasks, SSL without using human-provided labels achieves performance that is close to fully supervised approaches. Existing SSL research mostly focuses on perception tasks such as image classification, speech recognition, text classification, etc. SSL for reasoning tasks (e.g., symbolic reasoning on graphs, relational reasoning in computer vision, multi-hop reasoning in NLP) is largely ignored. In this workshop, we aim to bridge this gap. We bring together SSL-interested researchers from various domains to discuss how to develop SSL methods for reasoning tasks, such as how to design pretext tasks for symbolic reasoning, how to develop contrastive learning methods for relational reasoning, how to develop SSL approaches to bridge reasoning and perception, etc. Different from previous SSL-related workshops which focus on perception tasks, our workshop focuses on promoting SSL research for reasoning.

Time series is one of the fastest growing and richest types of data. In a variety of domains including dynamical systems, healthcare, climate science and economics, there have been increasing amounts of complex dynamic data due to a shift away from parsimonious, infrequent measurements to nearly continuous real-time monitoring and recording. This burgeoning amount of new data calls for novel theoretical and algorithmic tools and insights.

The goals of our workshop are to: (1) highlight the fundamental challenges that underpin learning from time series data (e.g. covariate shift, causal inference, uncertainty quantification), (2) discuss recent developments in theory and algorithms for tackling these problems, and (3) explore new frontiers in time series analysis and their connections with emerging fields such as causal discovery and machine learning for science. In light of the recent COVID-19 outbreak, we also plan to have a special emphasis on non-stationary dynamics, causal inference, and their applications to public health at our workshop.

Time series modeling has a long tradition of inviting novel approaches from many disciplines including statistics, dynamical systems, and the physical sciences. This has led to broad impact and a diverse range of applications, making it an ideal topic for the rapid dissemination of new ideas that take place at ICML. We hope that the diversity and expertise of our speakers and attendees will help uncover new approaches and break new ground for these challenging and important settings. Our previous workshops have received great popularity at ICML, and we envision our workshop will continue to appeal to the ICML audience and stimulate many interdisciplinary discussions.

Gather.Town:
Morning Poster: [ protected link dropped ]
Afternoon Poster: [ protected link dropped ]

While over many years we have witnessed numerous impressive demonstrations of the power of various reinforcement learning (RL) algorithms, and while much progress was made on the theoretical side as well, the theoretical understanding of the challenges that underlie RL is still rather limited. The best-studied problem settings, such as learning and acting in finite state-action Markov decision processes, or simple linear control systems fail to capture the essential characteristics of seemingly more practically relevant problem classes, where the size of the state-action space is often astronomical, the planning horizon is huge, the dynamics is complex, interaction with the controlled system is not permitted, or learning has to happen based on heterogeneous offline data, etc. To tackle these diverse issues, more and more theoreticians with a wide range of backgrounds came to study RL and have proposed numerous new models along with exciting novel developments on both algorithm design and analysis. The workshop's goal is to highlight advances in theoretical RL and bring together researchers from different backgrounds to discuss RL theory from different perspectives: modeling, algorithm, analysis, etc.

Modern machine learning models are often highly over-parameterized. The prime examples are neural network architectures achieving state-of-the-art performance, which have many more parameters than training examples. While these models can empirically perform very well, they are not well understood. Worst-case theories of learnability do not explain their behavior. Indeed, over-parameterized models sometimes exhibit "benign overfitting", i.e., they have the power to perfectly fit training data (even data modified to have random labels), yet they achieve good performance on the test data. There is evidence that over-parameterization may be helpful both computational and statistically, although attempts to use phenomena like double/multiple descent to explain that over-parameterization helps to achieve small test error remain controversial. Besides benign overfitting and double/multiple descent, many other interesting phenomena arise due to over-parameterization, and many more may have yet to be discovered. Many of these effects depend on the properties of data, but we have only simplistic tools to measure, quantify, and understand data. In light of rapid progress and rapidly shifting understanding, we believe that the time is ripe for a workshop focusing on understanding over-parameterization from multiple angles.

Gathertown room1 link: [ protected link dropped ]
Gathertown room2 link: [ protected link dropped ]