AI is integrated into scientific discovery ever more profusely to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain new insights that might not have been possible using traditional scientific methods alone. The main goal of this series of workshop is to discover synergy across a variety of scientific fields, encourage interdisciplinary discussions, and enhance the flow of knowledge between AI and Science communities. Throughout history, bridging seemly different fields has brought overarching benefits, with notable examples: entropy in thermodynamics and information theory, neuroscience and AI, and algorithms inspired by discoveries in science (e.g. genetic algorithm, simulated annealing and diffusion-based generative models). In the current AI era, successes of AI methods in different fields of science have alluded to the general effectiveness of collecting large simulated data, finding suitable architectures, enforcing invariances/equivariances, and utilizing foundation models. Our mission is to bring more scientists to attend ICML to share different perspectives on the use of AI, and to illuminate exciting research directions for AI researchers. In the following, we concentrate our discussion in this workshop on Scaling in AI for Science.Scaling models has addressed challenges once deemed insurmountable, including predicting 3D protein structures, simulating molecular behaviors, forecasting global climate shifts, discovering new physical laws, and proving theorems. As we enhance the scale of models, data sets, and application areas, there are challenges and opportunities that emerge which transcend individual scientific fields. This workshop aims to gather the AI for Science community from various disciplines to engage in meaningful dialogues about scaling AI for scientific breakthroughs. The expansion of model sizes offers a contrast to the scientific method, employed by scientists since the Renaissance, which emphasizes simplicity and reductionism. Although the primary goal of science is to unveil fundamental laws, the increased complexity of scaled models often complicates their interpretability. Nonetheless, these scaled models have shown extraordinary adaptability and efficiency in tackling complex challenges, providing significant benefits to both science and industry. As AI extends its reach to a broader range of scientific questions, our workshop will delve into the role of scalable AI in current scientific endeavors: what further contributions can we expect from AI in research? How can we effectively harness AI techniques? And how does AI influence the objectives and methods of science?To address these questions, we have invited a selection of speakers and panelists recognized for their understanding of scaling's impact on AI for Science. They will discuss how scaling introduces new dimensions and trade-offs in the development of methodologies, theoretical insights, interpretability, and discovery, sharing their expertise with the broader ML and scientific communities. These subjects will foster deep discussions on the critical impact and urgent inquiries surrounding scaling in AI for scientific exploration, drawing a diverse group of participants from the scientific, industrial, and ML research communities. Our objective is to uncover both the promising opportunities and the emerging challenges of this evolution, promoting a collaborative setting that encourages the sharing of insights and strategies across various fields. Significantly, our participants will benefit from cross-disciplinary synergies. Such synergy is vital for identifying the unique advantages and challenges of AI as a versatile tool for science advancement, sparking inspiration for its application in other untapped scientific domains.

Mathematical reasoning is one of the most advanced forms of human intelligence. Humans develop formal languages for rigorously describing mathematical problems and deriving mathematical knowledge. The machine learning community has endeavored to develop neural models with mathematical reasoning capabilities as humans. On the other hand, a shared vision in the community is that the models collaborate with humans for mathematical discoveries. The goal of this workshop is to bring together researchers working on various domains to discuss the progress and the future of applying AI technologies to mathematics. As mathematics is fundamental for almost all modern sciences (including computer science), a vast range of related topics are also within our scope. To this end, this workshop focuses on several crucial yet underexplored problems. Specifically, we are expecting attendants from various backgrounds, institutions, and disciplines to discuss areas related to the following: * Autoformalization and the reversed auto-informalization: How can we develop methods that improve the precision of the autoformalization process from natural language proof to formal proof, and as a dual process describing a formal proof in natural language?* Automated theorem proving: How do build consistent theorem proving? How do we relieve or solve the intermediate step errors in proving? * Automated theorem generation: Can neural models generate new and practically valid theorems? How do we take full advantage of such generated new theorems? * Code augmentation and auxiliary for mathematical reasoning: How can the handy and plentiful code data facilitate the models to conduct mathematical reasoning? * Formal verification and code generation: How can progress made in AI for Math help or be directly deployed to the field of formal verification? What are the common technical difficulties? How can AI systems be able to write provably correct code, given any (formal) specifications?In addition to the problem areas above, we also welcome research work related to the following topics:* Measurement: How do we measure autoformalization?* Reasoning in related areas: program synthesis, software verification, neurosymbolic reasoning, logical reasoning.* Applications: Applying mathematical reasoning techniques to sciences, finance, education, etc. Our workshop also includes an innovative challenge with three tracks for related mathematical reasoning problems:* Challenge/Track 1: Autoformalizaiton.* Challenge/Track 2: Automated theorem generation and proving.* Challenge/Track 3: Automated optimization problem-solving with code.

Gradients and derivatives are integral to machine learning, as they enable gradient-based optimization. In many real applications, however, models rest on algorithmic components that implement discrete decisions, or rely on discrete intermediate representations and structures. These discrete steps are intrinsically non-differentiable and accordingly break the flow of gradients. To use gradient-based approaches to learn the parameters of such models requires turning these non-differentiable components differentiable. This can be done with careful considerations, notably, using smoothing or relaxations to propose differentiable proxies for these components. With the advent of modular deep learning frameworks, these ideas have become more popular than ever in many fields of machine learning, generating in a short time-span a multitude of "differentiable everything", impacting topics as varied as rendering, sorting and ranking, convex optimizers, shortest-paths, dynamic programming, physics simulations, NN architecture search, top-k, graph algorithms, weakly- and self-supervised learning, and many more.

Aligning AI agents with human intentions and values is one of the main barriers to the safe and ethical application of AI systems in the real world. Current approaches mostly rely on highly questionable assumptions about the meaning of observed human feedback or interactions. These include assumptions about rationality in decision-making and belief forming, homogeneity of the population, and other restrictive feedback assumptions. However, the role of such modeling assumptions has mostly been neglected in the literature on AI alignment. In this workshop, we want to bring together perspectives from various disciplines besides ML, including computational social choice, behavioral psychology, and economics, to share experiences and perspectives on models of human feedback and their importance for human-AI alignment and collaboration.

In recent years, general-purpose AI has experienced a meteoric rise in capabilities and applications. This rise has continued to bring forth new safety challenges, requiring mitigation to ensure AI systems meet trustworthiness standards. In this workshop, we take a proactive approach to safety and focus on five emerging trends in AI and explore the challenges associated with deploying these technologies safely:1. Agentic AI: As AI agents become more autonomous, concerns about unintended consequences, ethical issues, and adversary exploitation emerge. How do we ensure these agents respect privacy, and adhere to safety protocols?2. Multimodal: With the evolution of AI systems to process and generate diverse modalities like audio, video, and images, concerns around content appropriateness, privacy, bias, and misinformation arise. How do we craft robust guidelines and security measures to tackle these challenges?3. Personalized Interactions: As conversational agents evolve for social and personal interaction, risks like data privacy breaches and echo chambers grow. How do we balance tailored experiences with user safety?4. Sensitive Applications: With AI’s integration into high-risk domains like legal, medical, and mental health, the stakes rise with risks such as overreliance on automation and potential catastrophic errors. How do we ensure that AI systems in these critical areas enhance decision-making without compromising human expertise and judgment? 5. Dangerous Capabilities: As AI's knowledge and understanding capabilities improve, these systems could be leveraged to extract or generate information about harmful applications or technologies, including bioweapons or cyber attack methods. How do we ensure that AI systems are designed with safeguards to prevent their misuse in creating or disseminating dangerous knowledge, while still allowing for beneficial research and innovation?We believe this next frontier of capabilities and applications raises new research questions: What does the next frontier in AI safety look like? How do we evaluate it? And how can we develop strong safeguards for tomorrow’s AI systems?Combatting the novel challenges of next generation AI systems necessitates new safety techniques, spanning areas such as synthetic data generation and utilization, content moderation, and model training methodologies. The proliferation of open-source and personalized models tailored for various applications widens the scope of deployments, and amplifies the already-urgent need for robust safety tools. Moreover, this diverse range of potential deployments entails complex trade-offs between safety objectives and operational efficiency. Taken together, there is a broad set of urgent and unique research challenges and opportunities to ensure the safety of the AI systems of tomorrow.Goal: In this workshop, we will bring together researchers across academia and industry working on improving safety and alignment of state-of-the-art AI systems as they are deployed. We aim for the event to facilitate sharing of challenges, best practices, new research ideas, data, and evaluations, that both practically aid development and spur progress in this area.

Multi-modal Foundation Model meets Embodied AI (MFM-EAI)In recent years, Multi-modal Foundation Models (MFM) such as CLIP, ImageBind, DALL·E 3, GPT-4V, and Gemini have emerged as one of the most captivating and rapidly advancing areas in AI, drawing significant attention and progressing swiftly. The open-source community for MFM has also seen vigorous growth, with the emergence of models and algorithms like LLaVA, LAMM, Stable Diffusion, and OpenFlamingo. These MFMs are now actively exploring ultimate application scenarios beyond traditional computer vision tasks.Recent studies have unveiled the immense potential these models hold in empowering embodied AI agents, marking the intersection of these fields with a multitude of open questions and unexplored territories. This workshop, MFM-EAI, is dedicated to exploring these critical challenges:- How can we train and evaluate MFM in open-ended environments?- What constitutes an effective system architecture for MFM-based Embodied AI Agents?- And importantly, how can MFM augment the perceptual and decision-making capabilities of these agents, balancing their high-level decision-making prowess with the nuanced requirements of low-level control in embodied systems?Topics include but are not limited to:- Training and evaluation of MFM in open-ended scenarios- Data collection for training Embodied AI Agents and corresponding MFM- Framework design for MFM-powered embodied agents- Decision-making in Embodied Agents empowered by MFM- Low-level control in Embodied Agents empowered by MFM- Evaluation and simulation of Embodied Agents- Limitations of MFM in empowering Embodied AI

This workshop aims to bring together various researchers to chart the course for the next generation of sequence models. The focus will be on better understanding the limitations of existing models like transformer architectures, recurrent neural networks, and state space models (e.g., S4, Mamba), as well as describing existing open problems. We will touch on topics such as memory, long-range context and in-context learning, optimization stability of these architectures, and their ability to represent different classes of problems. We will also cover interpretability and pragmatic aspects of getting these models to be efficient and perform well: how they should be scaled up, and the trade-offs and limitations imposed by current hardware. We will place additional emphasis on the discussion regarding how we should evaluate and benchmark sequential models at scale, for example, in the context of language or other domains like vision, audio, or biological signals.

Climate change is a major concern for human civilization, yet significant uncertainty remains in future warming, change in precipitation patterns, and frequency of climate extremes. Proper adaptation and mitigation demands accurate climate projections capable of simulating the atmosphere, ocean, land, and their interactions. Numerical models exhaustively tuned by domain scientists have been the gold standard for modeling both weather and climate because of their interpretability and ability to simulate “what-if” scenarios not present in the historical record. Although AI forecasts have started to make operational progress in weather prediction, climate projections are a harder problem. For example, High Impact-Low Likelihood events are undersampled in ERA5 reanalysis data, and substantial decadal variability in modes of climate variability (like the El-Niño Southern Oscillation) limit the ability of AI forecasts to reliably extrapolate into the future. This workshop seeks to accelerate progress on using machine learning to improve climate projections, emphasizing areas that domain scientists have deemed amenable to machine learning approaches. Examples include hybrid physics-ML climate models, where machine learning is used to emulate subgrid processes too expensive to resolve explicitly, and dynamical downscaling, where high-resolution climate variables are inferred from coarse-resolution models in a physically consistent manner. In service of this, our workshop will be accompanied by a $50,000 Kaggle competition on the ClimSim dataset (https://leap-stc.github.io/ClimSim/), which won the Outstanding Datasets and Benchmarks Paper award at NeurIPS 2023. We welcome submissions on machine learning topics that can advance earth system model development. Some examples include deep generative models, explainable AI, physics-informed neural networks, and uncertainty quantification. While machine learning is not new to the climate science community, dedicated opportunities for the cross-fertilization of ideas are rare, and machine learning experts motivated to make an impact may not be aware of domain science research directions most in need of their expertise. This workshop directly addresses both of these challenges.

Reinforcement learning has evolved into a dynamic and expansive field, attracting both theorists and experimentalists. While theorists and experimentalists in reinforcement learning share a common interest in advancing the field, their research objectives, methodologies, and challenges sometimes diverge significantly. This workshop aims to bridge this gap by bringing them closer together and to shed light on recent developments and synergies in both communities.

Modeling learning dynamics has long been a goal of the empirical science and theory communities in deep learning. These communities have grown rapidly in recent years, as our newly expanded understanding of the latent structures and capabilities of large models permits researchers to study these phenomena through the lens of the training process. Recent progress in understanding fully trained models can therefore enable understanding of their development and lead to insights that improve optimizer and architecture design, provide model interpretations, inform evaluation, and generally enhance the science of neural networks and their priors. We aim to foster discussion, discovery, and dissemination of state-of-the-art research in high-dimensional learning dynamics relevant to ML.

We invite participation in the 2nd Workshop on High-dimensional Learning Dynamics (HiLD), to be held as a part of the ICML 2024 conference. This year’s theme focuses on understanding how reasoning capabilities and internal structures develop over the course of neural network training; we encourage submissions related to our theme as well as other topics around the theoretical and empirical understanding of learning in high dimensional spaces. We will accept high quality submissions as poster presentations during the workshop, especially work-in-progress and state-of-art ideas.

We welcome any topics in pursuit of understanding how model behaviors evolve or emerge. Example topics include but are not limited to:

The emergence of interpretable behaviors (e.g., circuit mechanisms) and capabilities (e.g., compositionality and reasoning) Work that adapts tools from stochastic differential equations, high-dimensional probability, random matrix theory, and other theoretical frameworks to understand learning dynamics and phase transitions Scaling laws related to internal structures and functional differences Competition and dependencies among structures and heuristics, e.g., simplicity bias or learning staircase functions Relating optimizer design and loss landscape geometry to implicit regularization, inductive bias, and generalization

The workshop focuses on theory, methodology, and application of structured probabilistic inference and generative modeling, both of which are important topics in machine learning.Specifically, probabilistic inference addresses the problem of amortization,sampling, and integration of complex quantities from graphical models, while generative modeling captures the underlying probability distributions of a dataset. Apart from applications in computer vision, natural language processing, and speech recognition, probabilistic inference and generative modeling approaches have also been widely used in natural science domains, including physics, chemistry, molecular biology, and medicine. Beyond applications in these domains, the span of tasks of the methods have been expanding beyond probabilistic inference and generative model such as optimal control, decision making, sampling, optimization, etc.Despite the promising results, probabilistic methods face challenges when applied to highly structured data, which are ubiquitous in real-world settings, limiting the applications of such methods. This workshop aims to bring experts from diverse backgrounds and related domains together to discuss the applications and challenges of probabilistic methods. The workshop will emphasize challenges in encoding domain knowledge when learning representations, performing inference and generations. By bringing together experts from academia and industry, the workshop will provide a platform for researchers to share their latest results and ideas, fostering collaboration and discussion in the field of probabilistic methods.

Foundation models have become a cornerstone in the advancement of artificial intelligence, widely used across both academic and practical applications. Across domains, many challenging tasks require synthesizing information over thousands to millions of individual pieces of data, which may take many forms, including images, text, audio, genomes, etc. As a result, much recent work has focused on developing long-context models capable of processing, understanding, and generating responses based on extensive inputs. Enabling foundation models to process long contexts introduces several key challenges: (1) Computation efficiency: transformers, the predominate architecture for foundation models, incur a quadratic computational complexity with respect to the input length. (2) Lack of data: The development of long-context foundation models requires access to a large amount of long-sequence data, which is difficult to satisfy due to the limited availability of such collections. (3) Evaluation complexity: Evaluating the performance of long-context foundation models is inherently complex, as it is costly to collect, construct, or verify such evaluation data by humans.Our workshop aims to convene researchers to address these challenges, fostering discussions, developments, and evaluation of long-context foundation models across various AI disciplines.

This workshop aims to highlight translational ML research in biology and chemistry ML for real-world applications in life-and materials science. The goal is to bridge theoretical advanceswith practical applications and connect academic and industry researchers.

Biology and chemistry play a central role in understanding life, and are a fundamental pillar ofhuman well-being through their roles as medicines, materials, or agro-chemicals.

With increasingchallenges associated with climate change, growth of the global population, diseases associatedwith aging, and the global supply of food and energy, it is becoming increasingly urgent toaccelerate the pace at which technical discoveries can be made, and translated into practical solutions to these societal issues.

However, compared to other modalities such as images orlanguage, the study of biology and chemistry with machine learning is not as industriallyestablished. Multiple factors contribute to this delay. Different research questions require manylevels and scales of representation, from electronic structure to graph and point cloudrepresentations of (bio) molecules, to protein and nucleic acid sequences, crystals, omics data, celland tissue-level representations.

We envision abalanced scientific industrial and academic attendance, and propose committees and a lineup thatreflect a mix of top industry scientists, academic leaders and double-affiliated scientists, as well asemerging scientists and new voices in ML for healthcare, molecular-, life- and material sciences.We welcome a broad range of submissions, from dataset curation, analysis and benchmarking workhighlighting opportunities and pitfalls of current ML applications in health and materials, to novelmodels and algorithms unlocking capabilities previously thought available only through non-MLapproaches. We welcome all types of ML algorithms and models relevant for this problem space.

Lastly, we aim to integrate two areas - life and material sciences – as ML approaches in these areascan usually be adapted to one or the other discipline, and we want to encourage discussionbetween practitioners in the respective fields. Lastly, we are committed to create an inclusiveworkshop with broad representation across research areas, regions and beliefs.