(10 Oct 2022–28 Oct 2022)
Organizing Committee
Co-chairs
- Peter Bartlett (Simons Institute for the Theory of Computing, UC Berkeley)
- Soufiane Hayou (National University of Singapore)
- Hui Ji (National University of Singapore)
- Qianxiao Li (National University of Singapore)
- Gianmarco Mengaldo (National University of Singapore)
- Anqi Qiu (National University of Singapore)
- Jonathan Scarlett (National University of Singapore)
- Yong Sheng Soh (National University of Singapore)
- Csaba Szepesvari (Simons Institute for the Theory of Computing, University of Alberta)
- Vincent Y. F. Tan (National University of Singapore)
- Alexandre Hoang Thiery (National University of Singapore)
- Thomas, Xin Tong (National University of Singapore)
- Wanjie Wang (National University of Singapore)
- Angela Yao (National University of Singapore)
Contact Information
General Enquiries: ims-enquiry(AT)nus.edu.sg
Scientific Aspects Enquiries: qianxiao(AT)nus.edu.sg
Overview
The aim of this workshop is to bring together researchers in theoretical and applied machine learning, to share their work and to explore collaborative opportunities.
Workshop themes
- Deep Learning Theory
- Machine Learning Applications in Physical/Biological Sciences
- Bayesian Approaches in Machine Learning
- Interpretability, Fairness and Privacy in Machine Learning
- Reinforcement Learning Theory
Activities
- Tutorials (10–14 Oct)
- Scientific Sessions (Weeks of 17 Oct and 24 Oct): We will start with more application based talks in the week of 17 Oct and transitioning towards more theory focused sessions in the last week (24 Oct), but there will be a mix of both to potentially accommodate participants who can only come in a specific time window.
24 October 2022, Deepavali is a Public Holiday.
| Date | Abstract | |
|---|---|---|
| Tutorials | 10–14 October 2022 | View |
| Scientific Sessions | 17–28 October 2022 | View |
Venue
IMS Auditorium
Group Photo
