by Andrew M. Dai, Irene Chen, Gal Chechik

We are excited to present the list of tutorials selected to present at the NeurIPS 2024 conference! We look forward to a programme that we hope will engage the attendees with topics ranging from experimental design for AI researchers and meta-generation algorithms for LLMs through to cross-disciplinary insights into alignment. In this post, we will describe this year’s programme and our selection process.

Program

There will be 14 tutorials this year. All of them will be conducted in person to encourage active participation and some of them include panels to allow for a diverse range of discussion. The tutorials selected this year with their speakers are:

Flow Matching for Generative Modeling

Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu

Cross-disciplinary insights into alignment in humans and machines

Gillian Hatfield, Joel Z. Leibo, Dylan Hadfield-Menell

Experimental Design and Analysis for AI Researchers

Katherine Hermann, Jennifer Hu, Mike Mozer

Generating Programmatic Solutions: Algorithms and Applications of Programmatic Reinforcement Learning and Code Generation

Shao-Hua Sun, Levi Lelis, Xinyun Chen

Evaluating Large Language Models – Principles, Approaches, and Applications

Irina Sigler, Yuan (Emily) Xue, Bo Li

Beyond Decoding: Meta-Generation Algorithms for Large Language Models

Sean Welleck, Hailey Schoelkopf, Matthew Finlayson

Opening the Language Model Pipeline: A Tutorial on Data Preparation, Model Training, and Adaptation

Akshita Bhagia, Nathan Lambert, Kyle Lo

Dynamic Sparsity in Machine Learning: Routing Information through Neural Pathways

Edoardo M. Ponti, André F. T. Martins

Advancing Data Selection for Foundation Models: From Heuristics to Principled Methods

Jiachen T. Wang, Ruoxi Jia, Ludwig Schmidt

Watermarking for Large Language Model

Xuandong Zhao, Yu-Xiang Wang, Lei Li

Sandbox for the Blackbox: How LLMs Learn Structured Data?

Ashok Vardhan Makkuva, Bingbin Liu, Jason D. Lee

Out-of-Distribution Generalization: Shortcuts, Spuriousness, and Stability

Yoav Wald, Aahlad Puli, Maggie Makar

Causality for Large Language Models

Zhijing Jin, Sergio Garrido

Meaningful Evaluations of ML Privacy Techniques: What Are We Waiting For

Mimee Xu, Fazl Barez, Dmitrii Usynin

Selection process

We received 57 unique tutorial proposals this year with roughly half of them related to large language models. Each submission was reviewed by at least one tutorial chair with the accepted tutorials receiving at least two reviews. Review assignments were chosen based on the area of expertise and to avoid conflicts of interest. Each chair gave a score from 1-5 on three categories, the speaker/panelist quality, the topic (interestingness, importance, timeliness) and appropriateness (suitability for the NeurIPS audience).

Common reasons for lower scores included:

• Topics that were too niche for the general NeurIPS audience.
• Significant overlap with stronger proposals.
• Insufficient diversity among the speakers.
• Too much expectation of familiarity with specific software or hardware.
• Too much overlap with past tutorials.

Choosing the final tutorials from the high scoring ones was a challenging task and we appreciate all the work that went into these proposals. We will be doing run-throughs with the selected tutorials to offer advice and feedback.
Keep your eyes on a post-conference retrospective blog post with reflections on tutorials. Please feel free to reach out to us at tutorial-chairs@neurips.cc if you have any suggestions.