Despite rapid advances in machine learning, solving large-scale stochastic dynamic programming problems remains a significant challenge. The combination of neural networks with RL has opened new avenues for algorithm design, but the lack of theoretical guarantees of these approaches hinders their applicability to high-stake problems traditionally addressed using control theory, such as online supply chain optimization, industrial automation, and adaptive transportation systems. This workshop focuses on recent advances in developing a learning theory of decision (control) systems, that builds on techniques and concepts from two communities that have had limited interactions despite their shared target: reinforcement learning and control theory.

We are holding a one-day workshop on mechanistic interpretability -- the study of reverse-engineering trained neural networks to understand their learned algorithms and internal structure. Check out our website for more details, including our proposed topics for discussion, speakers and panellists!

Recent advancements in generative foundation models (FMs) such as large language models (LLMs) and diffusion models have propelled the capability of deep neural models to seemingly magical heights. Yet, the soaring growth in the model size and capability has also led to pressing concerns surrounding such modern AI systems. The scaling of the models significantly increases their energy consumption and deployment cost. Overreliance on AI may perpetuate existing inequalities and lead to widening discrimination against certain groups of people. The gap between the understanding of the internal workings of FMs and their empirical success has also reached an unprecedented level, hindering accountability and transparency.For decades, theoretical tools from statistics, information theory, and optimization have played a pivotal role in extracting information from unstructured data. Currently, the rapid pace of FM development has outstripped theoretical investigation, creating a potential gap between theoretical researchers and the challenges surrounding FMs. This workshop proposes a platform for bringing together researchers and practitioners from the foundation model and theory community (including statistics, information theory, optimization, and learning theory), to discuss advances and challenges in addressing these concerns, with a focus on responsible AI, efficiency, and principled foundations.

Excitement about the capabilities of generative-AI systems has touched nearly every corner of ML research and public life. Amid such exhilarating potential, there is also intensifying unease around the development and deployment of generative-AI systems. By now, it is well-known that generative models ingest vast quantities of intellectual property (IP) [8–10], which they can regurgitate verbatim [1–3, 11, 12]. Such memorization has been the continued focus of copyright-focused lawsuits [4], but memorization and copyright just scratch the surface of potential legal issues at play. In the report from our ICML workshop last year, we produced a taxonomy of emerging issues that touch on intent, privacy, misinformation and disinformation, and IP (more broadly) [5]. Indeed, based on the events of the past year alone — executive orders [13], lawsuits [4], new and amended laws [7], and labor strikes [6] — it has only become clearer that there are significant “technical, doctrinal, and policy challenges presented by law for Generative AI, and by Generative AI for law” [5]. Within this challenging and fast-moving landscape, GenLaw has played an important clarifying and cross-educational role. The first GenLaw workshop at ICML 2023 hosted over 400 attendees in person, and our workshop recording has been watched over 1k times. Collectively, our blog and workshop report have been viewed over 25k times. GenLaw has helped pose novel questions (and refine existing ones) that push the frontier of generative-AI system capabilities in ways that attend to important legal considerations. We have been told repeatedly that the keynotes, panels, and conversations at last year’s workshop have even changed the trajectories of numerous Ph.D. students’ research, and have sparked entire new lines of inquiry in law and policy.Building on our past success, our workshop will continue to develop a comprehensive and precise synthesis of the legal issues at play, and of the associated ML research questions that these issues raise. We will leverage ICML’s location in Vienna to widen the scope of our legal engagement to the UK and EU, centering keynotes and panel participation from UK and EU researchers and scholars. Drawing from the research program developed in last year’s workshop report [5], we will concentrate our program on issues of IP, mis-/dis-information, and privacy. Based on (1) enthusiasm from the community to hold another GenLaw workshop at ICML, (2) interest in response to soliciting speakers and PC members, and (3) the continued explosion of general public interest in generative AI, we expect around 300 attendees in person, and at least another 300 virtually.

The past few years has seen a surge of interest in reinforcement learning, with breakthrough successes of applying RL in games, robotics, chemistry, logistics, nuclear fusion and more. These headlines, however, blur the picture of what remains a brittle technology,with many successes relying on heavily engineered solutions. Indeed, several recent works have demonstrated that RL algorithms are brittle to seemingly mundane design choices. Thus, it is often a significant challenge to effectively apply RL in practice, especially on novel problems, limiting its potential impact and narrowing its accessibility. In this workshop, we want to bring together different communities working on solving these problems. A variety of distinct sub-communities spanning RL, Meta-Learning and AutoML havebeen working on making RL work “out-of-the-box” in arbitrary settings - this is the AutoRL setting. Recently, with the emergence of LLMs and their in-context learning abilities, they have significantly impacted all these communities. There are LLM agents tacklingtraditional RL tasks as well as few-shot RL agents increasing efficiency and generalization that arealso trying to automate RL. LLMs have also been influencing AutoML directly with papers such as OptFormer. However, there is currently little crossover between these communities. As such, we want to create the space to connect them and cross-pollinate ideas automating RL. We believe closer connections between these communities will ultimately lead to faster and more focused progress on AutoRL and an in-person workshop is the ideal way to allow for greater interaction between them. Through a mixture of diverse expert talks and opportunity for conversation, we hope to emphasize the many facets of current AutoRL approaches and where collaboration across fields is possible.

This workshop addresses the growing significance of preparing high quality datasets for the development of large-scale foundation models. With recent advancements highlighting the key role of dataset size, quality, diversity, and provenance in model performance, this workshop considers the strategies employed for enhancing data quality, including filtering, augmentation, and relabeling. The workshop draws upon the increasing interest in data-centric research. It seeks to advance understanding and methodologies for dataset composition and curation, ultimately fostering the development of more robust models capable of addressing diverse challenges across multiple domains and that can benefit the public.

Large Language Models (LLMs) have undoubtedly taken center stage in the AI revolution, showing impressive performance in a wide variety of tasks, including machine translation, standardized tests, and conversational chatbots. It is even more impressive to uncover that these models exhibit unpredictable capabilities in solving unseen tasks. This demonstration of emergent abilities, often credited to the scale of the parameters and data size in the case of LLMs, is being considered as the footprint of intelligence.The goal of this workshop is to assess and understand the position of current LLMs’ abilities in the landscape of intelligent systems, with a strong focus on cognitive abilities. By bringing in experts from different scientific disciplines, such as AI/ML, neuroscience, cognitive science, and psychology, we aim to discuss topics that include but not limited to:• Where do LLMs stand in terms of performance on cognitive tasks, such as reasoning, navigation, planning, and theory of mind?What are the fundamental limits of language models with respect to cognitive abilities?• How do LLMs fine-tuned on specific tasks end-to-end compare to augmented LLMs coupled withexternal modules?• What are the similarities and differences between mechanistic interpretability approaches in AI and inneuroscience? What do they tell us about similarities and differences between LLMs and human brains?• How can we improve existing benchmarks and evaluation methods to rigorously assess cognitiveabilities in LLMs?• Can multimodal and multiagent approaches address some of current limits of LLMs to cognitive tasks?We hope that this workshop will help identify the gaps and opportunities in the current LLM landscape and shape the path for the development of trustworthy and robust systems guided by cognitive science.

In-context learning (ICL) is an emerging capability of large-scale models, including large language models (LLMs) like GPT-3, to acquire new capabilities directly from the context of an input example without separate training or fine-tuning, enabling these models to adapt rapidly to new tasks, datasets, and domains. This workshop brings together diverse perspectives on this new paradigm to assess progress, synthesize best practices, and chart open problems. Core topics will include architectural and other inductive biases enabling in-context skill acquisition, and reliable evaluation of ICL in application domains including reinforcement learning, representation learning, and safe and reliable machine learning.

The past few years have seen the rapid development of Generative AI, with powerful foundation models demonstrating the ability to generate new, creative content in multiple modalities. Following breakthroughs in text and image generation, it is clear the next frontier lies in video. One challenging but compelling aspect unique to video generation is the various forms in which one could control such generation: from specifying the content of a video with text, to viewing a scene with different camera angles, or even directing the actions of characters within the video. We have also seen the use cases of these models diversify, with works that extend generation to 3D scenes, use such models to learn policies for robotics tasks or create an interactive environment for gameplay. Given the great variety of algorithmic approaches, the rapid progress, and the tremendous potential for applications, we believe now is the perfect time to engage the broader machine learning community in this exciting new research area. We thus propose the first workshop on Controllable Video Generation (CVG), focused on algorithms that can control videos with multiple modalities and frequencies, and the swathe of potential applications. We anticipate CVG would be uniquely relevant to ICML as it brings together a variety of different communities: from traditional computer vision, to safety and alignment, to those working on world models in a reinforcement learning or robotics setting. This makes ICML the perfect venue, where seemingly unrelated communities can join together and share ideas in this new emerging area of AI research.

There is a growing gap between machine learning (ML) research on biology-inspired problems and the actual broad-based use of ML in the lab or the clinic. This gap is especially pressing in the context of foundation models and other large ML models. Accessibility and efficiency concerns limit the adoption of these models by biologists and clinicians. Large ML models may require extensive GPU clusters to train, while most biological labs only have access to much more modest computational resources. The usability of these models for non-expert users is also a concern, as is the need to iteratively adapt these models based on lab discoveries. This workshop seeks to bring ML and biomedical researchers together to identify interdisciplinary approaches to design and apply large, complex ML models for biomedical discovery. We invite researchers from academia and industry to submit original papers to bridge the accessibility and efficiency gap between ML research and wet lab use. All accepted papers will be invited to present posters at the workshop, and a few will be invited to give individual spotlight presentations.

With the widespread adoption of machine learning in social technologies, there are increasingly complex interactions between humans, algorithmic decision-makers, and society at large. For instance, algorithmic decisions influence the information and opportunities that are available to individuals, the news they read, the job listings they are matched to, the credit lines they receive, and the social circle they form. On a macroscopic level, such decisions can therefore affect societal outcomes such as social mobility, mental health, polarization etc. At the same time, humans also influence algorithmic decision-makers, for instance, by expressing their preferences through observed behaviors which might be inconsistent or strategic. To understand long-term individual and societal outcomes resulting from these interactions, and to develop algorithms that mitigate undesired outcomes, it has therefore become increasingly important to model these complex interactions as a whole. The goal of this workshop is to bring together researchers from both academia and industry who work on modeling interactions between AI systems, humans, and society. We aim to cover a wide range of topics including both theory and practice. In particular, we encourage submissions on the following topics:- Feedback loops between human and algorithmic decisions, and their long-term impacts- Strategic behavior and its impact on algorithmic decision-making- Models for human utility/preferences in the presence of irrational behavior- Generative and foundation models for interpretable human behavior- Emergent social phenomena and complex systems- Modeling societal outcomes through multi-agent models, mean-field games, etc.- Fairness and algorithmic approaches to mitigate disparate impactWe will invite speakers and solicit contributed papers and posters covering the various facets of these interactions. We are targeting different communities/fields such as machine learning, network science, social systems, algorithmic game theory, economics. We expect that bringing these different communities together will result in exchange of ideas and stimulate open discussions about the current challenges and future directions.

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By recognizing that nearly all data is rooted in our physical world, and thus inherently grounded in geometry and physics, it becomes evident that learning systems should preserve this grounding throughout the process of representation learning in order to be meaningful. For example, preserving group transformation laws and symmetries through equivariant layers is crucial in domains such as computational physics, chemistry, robotics, and medical imaging. It leads to effective and generalizable architectures and improved data efficiency. Similarly, in generative models applied to non-Euclidean data spaces, maintaining the manifold structure is essential to obtain meaningful samples. Therefore, this workshop focuses on the principle of grounding in geometry, which we define as follows: A representation, method, or theory is grounded in geometry if it can be amenable to geometric reasoning, that is, it abides by the mathematics of geometry.

This is a workshop proposal, targeting the intersection of Agentic AI and Market/Incentives Design.Workshop Summary: Recent developments in foundation models have paved the way for the wide adoption of AI agents that interact with humans and each other. The cooperation and safety of those models are a necessity, especially as they gain autonomy and participate in high stakes markets as autonomous systems, making those markets "agentic." However, those agentic markets face significant challenges as most existing methods at improving their performance and robustness presume critical use of policy and regulation, which are insufficient and too slow for an economy driven by a mixture of human and algorithmic participants, especially in zero-shot scenarios.As we advance towards an AI-centric future, the emergence of markets, mechanisms, and mediation platforms dedicated to preference elicitation and resource allocation for those highly agentic systems is inevitable. We expect many existing multi-agent security and cooperation approaches to break in high-stakes situations where hyper-adversarial incentives are present. This is compounded by the emergence of complexity from AI interactions, exemplified by intricate interdependencies within agentic systems.Given this complexity, how can we fully understand and assess the associated risks? How can we improve the performance and robustness of these markets? It is essential to draw lessons from traditional markets with less agentic AI (e.g., finance), to achieve robust incentives and economic security in a post-foundation model world. We recognize the need to incorporate principles of cryptography and robust market design. However, the sufficiency of these approaches is not certain. We aim to identify the missing elements and treat the sudy of market design in presence of agentic AI as a scientific discipline.This workshop seeks to amalgamate insights from economics, mechanism design, game theory, and, crucially, real-world financial markets expertise for algorithmic agents to better prepare us for the inevitable mass adoption of agentic AI on mission critical jobs. We aspire for this workshop to enlighten participants about new agentic-driven risks and opportunities, fostering disruptive collaborations among economists, market stakeholders, and AI researchers.ICML is the perfect venue for this workshop as it’s the focal point for a wide range of AI researchers and industry practitioners who have informed opinions on agentic systems. This interdisciplinary assembly is crucial for stimulating discussions that blend market design and economics with practical insights from the auctions and finance sector. We envision ICML as the perfect platform to nurture a scientific understanding of agentic markets. It is also the prime setting for enabling influential decision-makers and researchers to exchange knowledge and receive feedback, thereby facilitating impactful changes in the real world.

Advanced Multi-modal Foundation Models (MFMs) and AI Agents, equipped with diverse modalities [1, 2, 3, 4, 15] and an increasing number of available affordances [5, 6] (e.g., tool use, code interpreter, API access, etc.), have the potential to accelerate and amplify their predecessors’ impact on society [7].

MFM includes multi-modal large language models (MLLMs) and multi-modal generative models (MMGMs). MLLMs refer to LLM-based models with the ability to receive, reason, and output with information of multiple modalities, including but not limited to text, images, audio, and video. Examples include Llava [1], Reka [8], QwenVL [9], LAMM [36],and so on. MMGMs refer to a class of MFM models that can generate new content across multiple modalities, such as generating images from text descriptions or creating videos from audio and text inputs. Examples include Stable Diffusion [2], Sora [10], and Latte [11]. AI agents, or systems with higher degree of agenticness, refer to systems that could achieve complex goals in complex environments with limited direct supervision [12]. Understanding and preempting the vulnerabilities of these systems [13, 35] and their induced harms [14] becomes unprecedentedly crucial.

Building trustworthy MFMs and AI Agents transcends adversarial robustness of such models, but also emphasizes the importance of proactive risk assessment, mitigation, safeguards, and the establishment of comprehensive safety mechanisms throughout the lifecycle of the systems’ development and deployment [16, 17]. This approach demands a blend of technical and socio-technical strategies, incorporating AI governance and regulatory insights to build trustworthy MFMs and AI Agents.

Topics include but are not limited to: - Adversarial attack and defense, poisoning, hijacking and security [18, 13, 19, 20, 21] - Robustness to spurious correlations and uncertainty estimation - Technical approaches to privacy, fairness, accountability and regulation [12, 22, 28] - Truthfulness, factuality, honesty and sycophancy [23, 24] - Transparency, interpretability and monitoring [25, 26] - Identifiers of AI-generated material, such as watermarking [27] - Technical alignment / control , such as scalable overslight [29], representation control [26] and machine unlearning [30] - Model auditing, red-teaming and safety evaluation benchmarks [31, 32, 33, 16] - Measures against malicious model fine-tuning [34] - Novel safety challenges with the introduction of new modalities