As machine learning models are introduced into every aspect of our lives, and potential benefits become abundant, so do possible catastrophic failures. One of the most common failure scenarios when deploying machine learning models in the wild, which could possibly lead to dire consequences in extreme cases, is the reliance of models on apparently unnatural or irrelevant features.
The issue comes up in a variety of applications: from the reliance of detection models for X-rays on scanner types and marks made by technicians in the hospital, through visual question answering models being sensitive to linguistic variations in the questions, the list of examples for such undesirable behaviors keeps growing.In examples like these, the undesirable behavior stems from the model exploiting a spurious correlation.

Following last year's workshop on Spurious Correlations, Invariance and Stability (SCIS), it is apparent that work on spurious correlations is a long-term effort that spans communities such as fairness, causality-inspired ML, and domains such as NLP, healthcare and many others. Hence we hope that this year's workshop, the second edition of SCIS, will help facilitate this long term effort across communities. The workshop will feature talks by top experts doing methodological work on dealing with spurious correlations, and an extended poster session to allow extensive discussion on work submitted to the workshop.

As models increase in size and training budget, they not only systematically improve in upstream quality, but also exhibit novel emergent capabilities. This increase in scale raises proportionate difficulties for practitioners: foundation model training and inference lie at a unique interdisciplinary crossroad, combining open problems in algorithms, system design, and software engineering.

Machine learning practitioners are key stakeholders here: on the one hand, researchers may contribute algorithmic insights and novel methods to improving training and inference of large models; on the other hand, novel research findings may be best demonstrated at scale—which may require training models as efficiently as possible to make the best use of available resources.

The goal of this workshop is to bring together interdisciplinary experts working on the emerging research questions and challenges associated with foundation model training and inference. We welcome submissions around training and inference systems/algorithms for foundation models, focusing on scaling-up or on reducing compute, time, memory, bandwidth, and energy requirements. Notably, we encourage submissions concerning the entire spectrum of foundation models: from BERT-sized Transformers, to large models with 100B+ parameters. Topics include but are not limited to:

* Training and inference systems, either distributed at large scale or in resource-constrained scenarios;
* Algorithms for improved training and inference efficiency;
* Systems for foundation models, such as novel programming languages or compilers.

Each year, machine learning (ML) advances are successfully translated to develop systems we now use regularly, such as speech recognition platforms or translation software. The COVID-19 pandemic has highlighted the urgency for translating these advances to the domain of biomedicine. Biological data has unique properties (high dimensionality, degree of noise and variability), and therefore poses new challenges and opportunities for methods development. To facilitate progress toward long-term therapeutic strategies or basic biological discovery, it is critical to bring together practitioners at the intersection of computation, ML, and biology.The ICML Workshop on Computational Biology (WCB) will highlight how ML approaches can be tailored to making both translational and basic scientific discoveries with biological data, such as genetic sequences, cellular features or protein structures and imaging datasets, among others. This workshop thus aims to bring together interdisciplinary ML researchers working in 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. We especially encourage interdisciplinary submissions that might not neatly fit into one of these categories.

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.

Systems that can learn interactively from their end-users are quickly becoming widespread in real-world applications. Typically humans provide tagged rewards or scalar feedback for such interactive learning systems. However, humans offer a wealth of implicit information (such as multimodal cues in the form of natural language, speech, eye movements, facial expressions, gestures etc.) which interactive learning algorithms can leverage during the process of human-machine interaction to create a grounding for human intent, and thereby better assist end-users. A closed-loop sequential decision-making domain offers unique challenges when learning from humans -– (1) the data distribution may be influenced by the choices of the algorithm itself, and thus interactive ML algorithms need to adaptively learn from human feedback, (2) the nature of the environment itself changes rapidly, (3) humans may express their intent in various forms of feedback amenable to naturalistic real-world settings, going beyond tagged rewards or demonstrations. By organizing this workshop, we attempt to bring together interdisciplinary experts in interactive machine learning, reinforcement learning, human-computer interaction, cognitive science, and robotics to explore and foster discussions on such challenges. We envision that this exchange of ideas within and across disciplines can build new bridges, address some of the most valuable challenges in interactive learning with implicit human feedback, and also provide guidance to young researchers interested in growing their careers in this space.

Had I left 5 minutes earlier, I would have caught the bus. Had I been driving slower, I would have avoided the accident. Counterfactual thoughts—“what if?” scenarios about outcomes contradicting what actually happened—play a key role in everyday human reasoning and decision-making. In conjunction with rapid advancements in the mathematical study of causality, there has been an increasing interest in the development of machine learning methods that support elements of counterfactual reasoning, i.e., they make predictions about outcomes that "could have been different". Such methods find applications in a wide variety of domains ranging from personalized healthcare and explainability to AI safety and offline reinforcement learning. Although the research at the intersection of causal inference and machine learning is blooming, there has been no venue so far explicitly focusing on methods involving counterfactuals. In this workshop, we aim to fill that space by facilitating interdisciplinary interactions that will shed light onto the three following questions: (i) What insights can causal machine learning take from the latest advances in cognitive science? (ii) In what use cases is each causal modeling framework most appropriate for modeling counterfactuals? (iii) What barriers need to be lifted for the wider adoption of counterfactual-based machine learning applications, like personalized healthcare?

Despite being widely used, global end-to-end learning has several key limitations. It requires centralized computation, making it feasible only on a single device or a carefully synchronized cluster. This restricts its use on unreliable or resource-constrained devices, such as commodity hardware clusters or edge computing networks. As the model size increases, synchronized training across devices will impact all types of parallelism. Global learning also requires a large memory footprint, which is costly and limits the learning capability of single devices. Moreover, end-to-end learning updates have high latency, which may prevent their use in real-time applications such as learning on streaming video. Finally, global backpropagation is thought to be biologically implausible, as biological synapses update in a local and asynchronous manner. To overcome these limitations, this workshop will delve into the fundamentals of localized learning, which is broadly defined as any training method that updates model parts through non-global objectives.

This new workshop will bring together interdisciplinary scientists and practitioners working at the intersections of machine learning (ML) to medicine, pathology and biology, for presenting new methods and solutions for healthcare challenges across the full range of multimodal, and often highly heterogeneous and complex patient data, to the wider ICML community. Topics of interest include, but are not limited to: Multimodal fusion and learning in medical imaging, digital pathology, computational biology, genetics, electronic healthcare records; Multimodal biomarkers for early prediction of disease onset, therapeutic response or disease recurrence; Benchmarking, domain shifts, and generalization of ML in multimodal healthcare data; ML for dealing with inherent sparsity, incompleteness and complexity of multimodal healthcare data; ML for ensuring fairness and reducing bias in healthcare applications; ML for privacy preservation in healthcare data; Co-creation and human-in-the-loop for ML in healthcare.

The recent breathtaking progress made in generative natural language processing (NLP) has been propelled by large language models and innovative learning methods that intersects machine learning (ML) and NLP such as Reinforcement Learning with Human Feedback (RLHF), leading to the creation of impressive chatbots like ChatGPT. However their lack of groundedness, factuality, and interoperability with tools and custom APIs limits them to mostly creative endeavors due to low fidelity and reliability. On the contrary, digital assistants in the real world such as Siri, Alexa, and Google Assistant can interface with proprietary APIs, but they still cover a relatively narrow set of use cases that are mostly simple single-turn interactions. Through the combination of each of their strengths, the goal of deploying truly conversational and capable digital assistants that are also trustworthy seems tantalizingly close. What are the remaining challenges for this goal, and how can the ML and NLP communities come together to overcome them? The goal of this workshop is to bring together machine learning researchers and dialogue researchers from academia and industry to encourage knowledge transfer and collaboration on these central questions to discover ideas that can further expand the use cases of conversational AI. The ideal outcome of the workshop is to identify a set of concrete research directions to enable the next generation of digital assistants.

As modern astrophysical surveys deliver an unprecedented amount of data, from the imaging of hundreds of millions of distant galaxies to the mapping of cosmic radiation fields at ultra-high resolution, conventional data analysis methods are reaching their limits in both computational complexity and optimality. Deep Learning has rapidly been adopted by the astronomical community as a promising way of exploiting these forthcoming big-data datasets and of extracting the physical principles that underlie these complex observations. This has led to an unprecedented exponential growth of publications combining Machine Learning and astrophysics. Yet, many of these works remain at an exploratory level and have not been translated into real scientific breakthroughs.Following a successful initial iteration of this workshop at ICML 2022, our continued goal for this workshop series is to bring together Machine Learning researchers and domain experts in the field of Astrophysics to discuss the key open issues which hamper the use of Deep Learning for scientific discovery.

This workshop aims to address fundamental problems in the young but potentially highly-impactful field of machine-learning-based methods for data compression and communication. We invite participates to exchange ideas on fundamental issues in neural compression such as the role of quantization and stochasticity in communication, characterization and estimation of information measures, and more resource-efficient models/methods. We aim to address these fundamental issues by bringing together researchers from various fields including machine learning, information theory, statistics, and computer vision.

This is the third edition of highly successful workshops focused on data-centric AI, following the success of the Data-Centric AI workshop at NeurIPS 2021 and DataPerf workshop at ICML 2022. Data, and operations over data (e.g., cleaning, debugging, curation) have been continually fueling the success of machine learning for decades. While historically the ML community has focused primarily on model development, recently the importance of data quality has attracted intensive interest from the community, including the creation of the NeurIPS dataset and benchmark track, several data-centric AI benchmarks (e.g., DataPerf), and the flourishing of data consortiums such as LAION, the community’s attention has been directed to the quality of data used for ML training and evaluation. The goal of this workshop is to facilitate these important topics in what we call Data-centric Machine Learning Research, which includes not only datasets and benchmarks, but tooling and governance, as well as fundamental research on topics such as data quality and data acquisition for dataset creation and optimization.

Progress in generative AI depends not only on better model architectures, but on terabytes of scraped Flickr images, Wikipedia pages, Stack Overflow answers, and websites. But generative models ingest vast quantities of intellectual property (IP), which they can memorize and regurgitate verbatim. Several recently-filed lawsuits relate such memorization to copyright infringement. These lawsuits will lead to policies and legal rulings that define our ability, as ML researchers and practitioners, to acquire training data, and our responsibilities towards data owners and curators.

AI researchers will increasingly operate in a legal environment that is keenly interested in their work — an environment that may require future research into model architectures that conform to legal requirements. Understanding the law and contributing to its development will enable us to create safer, better, and practically useful models.

We’re excited to share a series of tutorials from renowned experts in both ML and law and panel discussions, where researchers in both disciplines can engage in semi-moderated conversation.

Our workshop will begin to build a comprehensive and precise synthesis of the legal issues at play. Beyond IP, the workshop will also address privacy and liability for dangerous, discriminatory, or misleading and manipulative outputs. It will take place on 29 July 2023, in Ballroom B.

Artificial intelligence (AI) and Human Computer Interaction (HCI) share common roots: early work on conversational agents has laid the foundation for both fields. However, economic and political influences have driven these fields to remain separate in subsequent decades. The recent rise of data-centric methods in machine learning has propelled few-shot emergent AI capabilities, resulting in a raft of practical tools. In particular, modern AI techniques now power new ways for machines and humans to interact. Recently, a wave of HCI tasks have been proposed to the machine learning community, which direct AI research by contributing new datasets and benchmarks, and challenging existing modeling techniques, learning methodologies, and evaluation protocols. This workshop offers a forum for researchers to discuss these new research directions, identifying important challenges, showcasing new computational and scientific ideas that can be applied, sharing datasets/tools that are already available, or proposing those that should be further developed.

Duality is a pervasive and important principle in mathematics. Not only has it fascinated researchers in many different fields but it has also been used extensively in optimization, statistics, and machine-learning (ML), giving rise to powerful tools such as Fenchel duality in convex optimization, representer theorems in kernel methods and Bayesian nonparametrics, and dually-flat spaces in information geometry. Such applications have played an important role in the past, but lately we do not see much work on duality principles, especially in deep learning. For example, Lagrange duality can be useful for model explanation because it allows us to measure sensitivity of certain perturbations, but this is not yet fully exploited. This slowdown is perhaps due to a growing focus on nonconvex and nonlinear problems where duality does not seem to be directly applicable. There have not been any workshops on duality in recent years. With this workshop, we aim to revive the interest of the ML community in duality principles.The goal of the workshop is to bring together researchers working on various duality concepts from many different fields, and discuss new applications for modern machine learning, especially focusing on topics such as model understanding, explanation, and adaptation in deep learning and reinforcement learning.

This year's Indigenous in AI Workshop will cover four themes:

Theme 1: Large Models, Small Communities
Theme 2: Indigenous Data
Theme 3: The State of the Art in Indigenous AI
Theme 4: AI as a Tool to Empower Marginalized Communities

Each theme will have speakers talking to these themes followed by discussion. Our aim to highlight the key issues facing indigenous and marginalized communities, how these communities are leading their own change, and strategies for working together and catalyzing changes in the wider industry.

More details coming soon.