What’s coming up at #NeurIPS2024?

The thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024) will take place in Vancouver, Canada, from Tuesday 10 December to Sunday 15 December. There is a bumper programme of events, including invited talks, orals, posters, tutorials, workshops, and socials, not to mention AIhub’s session on science communication.

Invited talks

There are seven invited talks this year:
Alison Gopnik – The Golem vs. stone soup: Understanding how children learn can help us understand and improve AI
Sepp Hochreiter – Toward industrial artificial intelligence
Fei-Fei Li – From seeing to doing: Ascending the ladder of visual intelligence
Lidong Zhou – A match made in silicon: The co-evolution of systems and AI
Arnaud Doucet – From diffusion models to Schrödinger bridges
Danica Kragic – Learning for interaction and interaction for learning
Rosalind Picard – How to optimize what matters most?

Affinity group workshops

The following affinity group workshops will take place on Tuesday 10 – Thursday 12 December:

• New in ML – Tuesday 10
• Global South in AI – Tuesday 10
• Affinity Joint Poster Session – Tuesday 10
• Muslims in ML – Tuesday 10
• Black in AI – Tuesday 10
• LatinX in AI – Tuesday 10
• Women in Machine Learning – Tuesday 10
• Queer in AI – Wednesday 11
• Neurodiversity Workshop – Thursday 12
• Indigenous in AI/ML – Thursday 12
• Bridging the Future – Thursday 12

Science communication for AI researchers – an introduction

We (AIhub) will be running a short course on science communication on Tuesday 10 December. Find out more here.

Tutorials

There will be a total of 14 tutorials this year. These will be held on Tuesday 10 December.

• Evaluating Large Language Models – Principles, Approaches, and Applications, Bo Li, Irina Sigler, Yuan Xue
• Dynamic Sparsity in Machine Learning: Routing Information through Neural Pathways, Edoardo Maria Ponti, André Martins
• Opening the Language Model Pipeline: A Tutorial on Data Preparation, Model Training, and Adaptation, Kyle Lo, Akshita Bhagia, Nathan Lambert
• Watermarking for Large Language Models, Yu-Xiang Wang, Lei Li, Xuandong Zhao
• Causality for Large Language Models, Zhijing Jin, Sergio Garrido
• Flow Matching for Generative Modeling, Ricky T. Q. Chen, Yaron Lipman, Heli Ben-Hamu
• Experimental Design and Analysis for AI Researchers, Michael Mozer, Katherine Hermann, Jennifer Hu
• PrivacyML: Meaningful Privacy-Preserving Machine Learning and How To Evaluate AI Privacy, Mimee Xu, Dmitrii Usynin, Fazl Barez
• Advancing Data Selection for Foundation Models: From Heuristics to Principled Methods, Jiachen (Tianhao) Wang, Ludwig Schmidt, Ruoxi Jia
• Cross-disciplinary insights into alignment in humans and machines, Gillian Hadfield, Dylan Hadfield-Menell, Joel Leibo, Rakshit Trivedi
• Generating Programmatic Solutions: Algorithms and Applications of Programmatic Reinforcement Learning and Code Generation, Levi Lelis, Xinyun Chen, Shao-Hua Sun
• Out-of-Distribution Generalization: Shortcuts, Spuriousness, and Stability, Maggie Makar, Aahlad Manas Puli, Yoav Wald
• Beyond Decoding: Meta-Generation Algorithms for Large Language Models, Matthew Finlayson, Hailey Schoelkopf, Sean Welleck
• Sandbox for the Blackbox: How LLMs Learn Structured Data?, Bingbin Liu, Ashok Vardhan Makkuva, Jason Lee

Find out more about the tutorials here.

Workshops

The workshops will take place on Saturday 14 and Sunday 15 December:

• Audio Imagination: NeurIPS 2024 Workshop AI-Driven Speech, Music, and Sound Generation
• NeuroAI: Fusing Neuroscience and AI for Intelligent Solutions
• Symmetry and Geometry in Neural Representations
• Adaptive Foundation Models: Evolving AI for Personalized and Efficient Learning
• GenAI for Health: Potential, Trust and Policy Compliance
• Language Gamification
• MATH-AI: The 4th Workshop on Mathematical Reasoning and AI
• Generative AI and Creativity: A dialogue between machine learning researchers and creative professionals
• Statistical Frontiers in LLMs and Foundation Models
• 3rd Workshop on New Frontiers in Adversarial Machine Learning (AdvML-Frontiers)
• 5th Workshop on Self-Supervised Learning: Theory and Practice
• AI4Mat-2024: NeurIPS 2024 Workshop on AI for Accelerated Materials Design
• Attributing Model Behavior at Scale (ATTRIB)
• UniReps: Unifying Representations in Neural Models
• Table Representation Learning Workshop (TRL)
• Workshop on Responsibly Building Next Generation of Multimodal Foundation Models
• The Fourth Workshop on Efficient Natural Language and Speech Processing (ENLSP-IV): Highlighting New Architectures for Future Foundation Models
• AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond
• Workshop on Scalable Continual Learning for Lifelong Foundation Models
• Causality and Large Models
• Fine-Tuning in Modern Machine Learning: Principles and Scalability
• Mathematics of Modern Machine Learning (M3L)
• Workshop on Behavioral Machine Learning
• Algorithmic Fairness through the lens of Metrics and Evaluation
• Bayesian Decision-making and Uncertainty: from probabilistic and spatiotemporal modeling to sequential experiment design
• Pluralistic Alignment Workshop
• Socially Responsible Language Modelling Research (SoLaR)
• Workshop on Video-Language Models
• NeurIPS 2024 Workshop: Machine Learning and the Physical Sciences
• 2nd Workshop on Touch Processing: From Data to Knowledge
• Safe Generative AI
• Scientific Methods for Understanding Neural Networks: Discovering, Validating, and Falsifying Theories of Deep Learning with Experiments
• Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI
• Regulatable ML: Towards Bridging the Gaps between Machine Learning Research and Regulations
• Foundation Models for Science: Progress, Opportunities, and Challenges
• International Workshop on Federated Foundation Models in Conjunction with NeurIPS 2024 (FL@FM-NeurIPS’24)
• D3S3: Data-driven and Differentiable Simulations, Surrogates, and Solvers
• Foundation Model Interventions
• Machine Learning for Systems
• Tackling Climate Change with Machine Learning
• Intrinsically Motivated Open-ended Learning (IMOL)
• Compositional Learning: Perspectives, Methods, and Paths Forward
• Machine Learning in Structural Biology
• ML with New Compute Paradigms
• Time Series in the Age of Large Models
• Multimodal Algorithmic Reasoning Workshop
• NeurIPS’24 Workshop on Causal Representation Learning
• Interpretable AI: Past, Present and Future
• AI for New Drug Modalities
• System-2 Reasoning at Scale
• The First Workshop on Large Foundation Models for Educational Assessment
• Red Teaming GenAI: What Can We Learn from Adversaries?
• Workshop on Machine Learning and Compression
• Optimization for ML Workshop
• Workshop on Open-World Agents: Synnergizing Reasoning and Decision-Making in Open-World Environments (OWA-2024)
• Towards Safe & Trustworthy Agents

Find out more about the workshops here.

Accepted papers and other events

• Accepted paper list
• Datasets and benchmarks track
• Competition track
• Creative AI track

Lucy Smith is Senior Managing Editor for AIhub.

Lucy Smith is Senior Managing Editor for AIhub.