by Saadia Gabriel, Andrew Gordon Wilson, Marzyeh Ghassemi
We are excited to announce the tutorials selected for presentation at the NeurIPS 2023 conference! We look forward to an engaging program, spanning many exciting topics, including human-AI collaboration, diffusion models, automated theorem proving, efficient deployment of large language models, AI governance for accountability, and others. In this blog post, we detail the upcoming program and our selection process.
There will be 14 tutorials this year. To encourage active participation and facilitate community-building, we restricted tutorials to an in-person format. Another important feature of this year’s tutorials is the inclusion of a panel discussion. These panels open up the conversation to allow for diverse perspectives on the tutorial topic and lively exchange of ideas. The 2023 tutorials are:
Machine Learning for Theorem Proving
Speakers: Emily First, Albert Q. Jiang, Kaiyu Yang
Governance & Accountability for ML: Existing Tools, Ongoing Efforts, & Future Directions
Speakers: Emily Black, Hoda Heidari, Dan Ho
Application Development using Large Language Models
Speakers: Andrew Ng, Isa Fulford
Data-Centric AI for reliable and responsible AI: from theory to practice
Speakers: Mihaela van der Schaar, Isabelle Guyon, Nabeel Seedat
Do You Prefer Learning with Preferences?
Speakers: Aditya Gopalan, Aadirupa Saha
How to Work With Real Humans in Human-AI Systems
Speakers: Elizabeth Bondi-Kelly, Krishnamurthy (Dj) Dvijotham, Matthew E. Taylor
Language Models Meet World Models
Speakers: Zhiting Hu, Tianmin Shu
Exploring and Exploiting Data Heterogeneity for Prediction and Decision-Making
Speakers: Peng Cui, Hongseok Namkoong, Jiashuo Liu, Tiffany Cai
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for ML
Speakers: Michał Dereziński, Michael Mahoney
Reconsidering Overfitting in the Age of Overparameterized Models
Speakers: Spencer Frei, Vidya Muthukumar, Fanny Yang
Contributing to an Efficient and Democratized Large Model Era
Speakers: James Demmel, Yang You
Latent Diffusion Models: Is the Generative AI Revolution Happening in Latent Space?
Speakers: Karsten Kreis, Ruiqi Gao, Arash Vahdat
What can we do about NeurIPS Reviewer #2? Challenges, Solutions, Experiments and Open Problems in Peer Review
Speaker: Nihar Shah
Data Contribution Estimation for Machine Learning
Speakers: Stephanie Schoch, Ruoxi Jia, Yangfeng Ji
We received 54 proposal submissions this year. Each submission was reviewed by the Tutorial chairs, with the chair reviewing assignments based on expertise and avoiding conflicts of interest. Each chair gave a score between 1 (strong reject) to 10 (strong accept) to encapsulate their overall impression of the proposal. We then shortlisted the submissions that had received an average score of 5 or higher and discussed when there were disagreements between the initial reviews. A third review was then obtained from a different Tutorial Chair to finalize the decision to accept or reject a proposal. We accepted 14 proposals with this process.
Some relatively common reasons for low scores included (but were not limited to):
- The topic was too niche for a very broad audience.
- There were stronger submissions covering the same topic.
- Tutorial was not focused enough on core skills, or was formulated more as a workshop or talk.
- The tutorial was too focused on the work of a particular speaker or panelist.
- Low diversity, especially gender diversity.
- Guidelines were not followed (e.g. no panel included).
In the call for proposals, we emphasized the need for diversity in the teaching teams and panels. This was met with an overwhelming positive response. We look forward to tutorials that are representative of many NeurIPS community members.
Stay tuned for a post-conference retrospective blog post with reflections on the tutorials and thoughts for next year. You can share your tutorial experiences for a potential feature in the blog here.