Workshops

San Diego Mexico City Workshops 56 Events Workshop Algorithmic Collective Action Elliot Creager · Nicholas Vincent · Celestine Mendler-Dünner · William Agnew · Hanlin Li · Ulrich Aïvodji Dec 6, 8:00 AM - 5:00 PM Upper Level Room 4 The study of collective action has a long history in economics and sociology as a way for groups of people to impact markets and the political arena. Algorithmic collective action focuses on the study of such coordinated efforts in algorithmically-mediated sociotechnical systems. How can participants of AI systems coordinate towards a common good? We offer a platform to discuss new ideas and help define the foundational research directions for this emerging topic through interdisciplinary discussions between core ML researchers, scholars from the social sciences, community stakeholders and advocates. Show more View full details Workshop Embodied World Models for Decision Making Yunbo Wang · Qi Wang · Mengyue Yang · Shenyuan Gao · Huazhe Xu · Xin Jin · Mingqi Yuan · Nedko Savov · Guozheng Ma · Bo Liu · Siheng Chen · Yongquan Hu · Jenny Zhang · Minting Pan · Luc V Gool Dec 6, 8:00 AM - 5:00 PM Upper Level Room 30A-E World models infer and predict real-world dynamics by modeling the external environment, and have become a cornerstone of embodied artificial intelligence. They have powered recent progress in decision-making and planning for interacting agents. This workshop aims to bring together researchers working at the intersection of generative modeling, reinforcement learning, computer vision, and robotics to explore the next generation of embodied world models—models that enable agents to understand, predict, and interact with the world through learned models. By focusing on embodiment and decision-making, this workshop seeks to advance world models beyond passive prediction, toward active, goal-driven interaction with the physical and virtual world. By emphasizing embodiment and decision-making, we aim to move beyond passive sequence prediction toward goal-directed interaction with both physical and simulated worlds. Show more View full details Workshop Reliable ML from Unreliable Data Andrew Ilyas · Alkis Kalavasis · Anay Mehrotra · Manolis Zampetakis Dec 6, 8:00 AM - 5:00 PM Upper Level Room 2 Distributions shift, chatbots get jail‑broken, users game algorithms — how do we build reliable machine learning when data are missing, corrupted, or strategically manipulated? This workshop bridges theory and practice to tackle these challenges, bringing together researchers working on distribution shift, adversarial robustness, and strategic behaviour to chart principled yet deployable solutions for Reliable ML from Unreliable Data. Show more View full details Workshop Structured Probabilistic Inference and Generative Modeling Yuanqi Du · Dinghuai Zhang · Jiajun He · Heli Ben-Hamu · Francisco Vargas · Yunan Yang · Animashree Anandkumar · Arnaud Doucet · José Miguel Hernández-Lobato Dec 6, 8:00 AM - 5:00 PM Upper Level Ballroom 20C View full details Workshop AI4Mat-NeurIPS-2025: NeurIPS 2025 Workshop on AI for Accelerated Materials Design Santiago Miret · ALEXANDRE DUVAL · Rocío Mercado · Emily Jin · N M Anoop Krishnan · Kevin Maik Jablonka · Marta Skreta · Stefano Martiniani Dec 6, 8:00 AM - 5:00 PM Upper Level Room 29A-D AI4Mat-NeurIPS-2025 explores applications of artificial intelligence (AI) to materials via: 1. AI-Guided Materials Design; 2. Automated Chemical Synthesis; 3. Automated Material Characterization. AI4MatNeurIPS-2025 emphasizes structured, expert-driven dialogue on making advanced machine learning more impactful for real-world materials discovery. To that end, AI4Mat-RLSF (Research Learning from Speaker Feedback) creates a new structured discussion format where spotlight presenters receive curated, in-depth feedback from invited discussants. Further, the AI4Mat Frontiers & Benchmarking session brings together a diverse and distinguished set of speakers to critically examine current benchmarks, present state-of-the-art methods, and identify emerging opportunities and current limitations in AI-driven materials design. Show more View full details Workshop ML for Systems Dan Zhang · Xinlei XU · Mangpo Phothilimthana · Divya Mahajan · Haoran Qiu · Patrick Musau Dec 6, 8:00 AM - 5:00 PM Upper Level Room 5AB The 9th Machine Learning for Systems (ML for Systems) workshop brings together researchers and practitioners applying machine learning to core computer systems challenges. This year, we focus on three themes: (1) using LLMs and agentic workflows for systems tasks such as program synthesis and adaptive optimization; (2) applying ML to manage the complexity of large-scale training and serving of multimodal and reasoning models; and (3) leveraging ML for sustainable computing, including energy-, power-, and carbon-aware optimization. The workshop will feature invited talks, contributed presentations, and discussions aimed at advancing the frontier of ML for Systems research. Show more View full details Workshop Differentiable Learning of Combinatorial Algorithms: From Theory To Practice Cathy Wu · Nikolaos Karalias · Yusu Wang · Indradyumna Roy · Abir De Dec 6, 8:00 AM - 5:00 PM Upper Level Room 25ABC View full details Workshop ML x OR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making Jose Blanchet · Jing Dong · Henry Lam · Min-hwan Oh · Qiaomin Xie · Yao Xie · Assaf Zeevi · Enlu Zhou Dec 6, 8:00 AM - 5:00 PM Upper Level Room 26AB Much of traditional decision-making science is grounded in the mathematical formulations and analyses of structured systems to recommend decisions that are optimized, robust, and uncertainty-aware. This scientific approach, rooted in the field of Operations Research (OR), has evolved through decades of advancements in stochastic modeling, computational simulation and optimization, and exhibits key strengths in methodological rigor and uncertainty encoding. On the other hand, recent advances in the AI/ML space have eschewed this model-based paradigm and increasingly embraced, to great success, model-free algorithmic design frameworks. This workshop, which is the first NeurIPS workshop explicitly themed and structured on ML-OR synergization, aspires to present recent developments, challenges and emerging research to accelerate ML-OR synthesis. By integrating ML into established OR methodologies, we have the opportunities to produce more data-centric and adaptive solutions for complex decision-making tasks that could propel, in a much faster-paced manner, the frontier of "optimality" across many relevant applications. Concomitantly, the goal is also to explore how model-based principled OR approaches can help alleviate issues revolving around "black box" systems, and provide paths to enhance interpretability, trust, and performance analysis. Show more View full details Workshop UniReps: Unifying Representations in Neural Models Marco Fumero · Zorah Laehner · Irene Cannistraci · Clémentine Dominé · Bo Zhao · Alex Williams Dec 6, 8:00 AM - 5:00 PM Upper Level Ballroom 20D When, how and why do different neural models learn the same representations? New findings in neuroscience and artificial intelligence reveal a shared pattern: whether in biological brains or artificial models, different learning systems tend to create similar representations when subject to similar stimuli. The emergence of these similar representations is igniting a growing interest in the fields of neuroscience and artificial intelligence, with both fields offering promising directions for their theoretical understanding. These include analyzing the learning dynamics in neuroscience and studying the problem of identifiability in the functional and parameter space in artificial intelligence. While the theoretical aspects already demand investigation, the practical applications are equally compelling: aligning representations allows for model merging, stitching and reuse, while also playing a crucial role in multi-modal scenarios. Furthermore, studying the features that are universally highlighted by different learning processes brings us closer to pinpoint the invariances that naturally emerge from learning models, possibly suggesting ways to enforce them. The objective of the workshop is to discuss theoretical findings, empirical evidence and practical applications of this phenomenon, benefiting from the cross-pollination of different fields (ML, Neuroscience, Cognitive Science) to foster the exchange of ideas and encourage collaborations. In conclusion, our primary focus is to delve into the underlying reasons, mechanisms, and extent of similarity in internal representations across distinct neural models, with the ultimate goal of unifying them into a single cohesive whole. Show more View full details Workshop AI Virtual Cells and Instruments: A New Era in Drug Discovery and Development Quanquan Gu · Michelle Li · Xuefeng Liu · Chong Liu · Abhishek Pandey · Ji Won Park · Nataša Tagasovska · Marinka Zitnik Dec 6, 8:00 AM - 5:00 PM Upper Level Room 28A-E As the US FDA phases out animal testing requirements for drug discovery and development, AI tools will become widely adopted to simulate the effects of candidate drugs. We posit that building virtual cells and instruments with AI is poised to transform drug discovery and development by enabling large-scale simulation and interrogation of molecules, cells, and tissues. In our workshop, we will collaboratively define and promote this emerging scientific paradigm of AI to accelerate the drug discovery and development process in this new era. Show more View full details Workshop Machine Learning and the Physical Sciences Nicole Hartman · Garrett Merz · Vinicius Mikuni · Mariel Pettee · Sebastian Wagner-Carena · Antoine Wehenkel · Atilim Gunes Baydin · Kyle Cranmer · Siddharth Mishra-Sharma · Benjamin Nachman · Brian Nord · Savannah Thais Dec 6, 8:00 AM - 5:00 PM Upper Level Ballroom 6CF The Machine Learning and the Physical Sciences (ML4PS) workshop at NeurIPS is a unique gathering space for the growing community spearheading cross-cutting research topics at the intersection of machine learning (ML) and the physical sciences (PS). This includes the applications of ML to problems in the physical sciences (ML for PS) as well as developments in ML motivated by physical insights (PS for ML). The physical sciences are defined inclusively, including but not limited to physics, astronomy, cosmology, chemistry, biophysics, materials science, and Earth science. Join us to discuss the latest research at the convergence of these fields! Show more View full details Workshop Deep Learning for Code in the Agentic Era Zijian Wang · Giovanni Zappella · Qian Liu · Zora Wang · Wen-Ding Li · Wasi Uddin Ahmad · Binyuan Hui Dec 6, 8:00 AM - 5:00 PM Exhibit Hall G,H Deep learning for code has progressed from focused tasks—such as completion, repair, synthesis, and explanation to tackling complex, end-to-end software–engineering problems. A key recent breakthrough is the rise of coding agents. Unlike single-shot models, these systems plan, reason, explore, and invoke external tools to assist throughout the software-development lifecycle: adding features, refactoring, debugging, finding vulnerabilities, optimizing performance, summarizing code, and answering repository-level questions. Their growing versatility demands rigorous evaluation and a deeper understanding of their capabilities, limits, risks, and broader social impact. Building on momentum from both academia and industry (e.g. Google, OpenAI, Anthropic, SWE-Agent, OpenHands), we propose the 4th Deep Learning for Code (DL4C) workshop with a dedicated focus on coding agents. This workshop will provide a timely forum where researchers and practitioners can design and stress-test robust coding agents, discover novel applications and emergent behaviors, establish principled benchmarks and evaluation methods, study human–agent collaboration at scale, and advance the responsible, safe deployment of autonomous coding tools. Show more View full details Workshop AI for non-human animal communication Ellen Gilsenan-McMahon · Brittany Solano · Olivier Pietquin · Burooj Ghani · Lauren Harrell · Sara Keen · Vincent Dumoulin · Nicolas Mathevon · Benjamin Hoffman · Milad Alizadeh Dec 6, 8:00 AM - 5:00 PM Upper Level Room 9 The past few years have seen an unprecedented surge in both the availability of bioacoustic data and the sophistication of AI/machine learning models. This convergence presents a unique window of opportunity to revolutionize our understanding of animal communication and biodiversity. However, achieving this requires a conscious effort to integrate the disciplines of AI/Machine Learning and Ethology. This workshop will explore the intersection of artificial intelligence (AI) and bioacoustics, aiming to address challenges in processing complex bioacoustic data and interpreting animal signals in order to advance our understanding of non-human animal communication. Join us for a poster session, keynote talks and a panel discussion as we explore key opportunities to use AI to decipher animal languages and thus deepen our understanding of the natural world. Show more View full details Workshop AI and ML for Next-Generation Wireless Communications and Networking (AI4NextG @ NeurIPS’25) Cong Shen · Christopher Brinton · Gauri Joshi · Hyeji Kim · Osvaldo Simeone · Shiqiang Wang · Taesang Yoo · Jun Zhang Dec 6, 8:00 AM - 5:00 PM Upper Level Room 10 The field of wireless communications and networking is undergoing a paradigm shift, driven by the growing potential of Artificial Intelligence (AI) and Machine Learning (ML) to redefine traditional system design principles. This workshop aims to catalyze interest and foster collaboration between the AI/ML and wireless communications communities. The timing of this workshop is especially significant, as the next-generation (NextG) wireless standardization efforts (such as 6G and WiFi 9) are just getting started, with AI-native technologies expected to play a central role across all aspects of the wireless ecosystem – from radio access to network management and edge intelligence. NextG represents a foundational shift in global infrastructure, enabling ultra-fast, low-latency, and intelligent connectivity that will power future applications in AI, robotics, immersive environments, and autonomous systems. These technologies offer unprecedented opportunities to both drive and benefit many applications, from healthcare and transportation to industrial automation and environmental monitoring. The economic and societal implications are vast: NextG networks will underlie trillions in global GDP impact, bridge digital divides, and shape how billions of people interact with technology and each other in the decades to come. Despite the clear promise, a significant disconnect exists between the AI/ML and wireless resea