Regret-based learning algorithms have found applications in various environments including stochastic, adversarial and multi-agent ones. While optimal convergence rates were known in the stochastic and adversarial settings, the corresponding results in the multi-agent settings have started to appear only recently.
The aim of this workshop is to showcase recent trends and advances in regret-based learning algorithms in multi-agent competitive environments.
The program will consist of two tutorials, 15 invited talks, and poster presentations. For participants interested in presenting their work in the form of a poster and a flash-talk, the call for posters has all the details including the possibility of applying for a grant.
This workshop is part of the thematic semester Stochastic control and learning for complex networks (SOLACE) funded by Labex CIMI
The program, the abstracts and the slides are online. The book of abstracts including those of the posters is also available for download.
Tutorials
- Simina Branzei (Purdue Univ., US)
- Sylvain Sorin (Sorbonne Univ., France)
Invited talks
- Venkat Anantharam (UC Berkeley, US)
- Galit Ashkenazi-Golan (LSE, UK)
- Martin Bichler (TU Munich, Germany)
- Tommaso Cesari (Univ. of Ottawa, Canada)
- Julien Grand-Clement (HEC, France)
- Sergiu Hart (Hebrew Univ. of Jerusalem, Israel)
- Chi Jin (Princeton, US) -- online talk
- Sam Jindani (NUS, Singapore)
- Maryam Kamgarpour (EPFL, Switzerland)
- Panayotis Mertikopoulous (CNRS, France)
- Mehryar Mohri (CIMS, US)
- Vianney Perchet (ENSAE, France)
- Marco Scarsini (Luiss University, Italy)
- Bassel Tarbush (Univ. of Oxford, UK)
- Long Tran-Thanh (Univ. of Warwick, UK)
Registration is free but mandatory.
Important Dates
- Deadline for poster submissions (including grant applications):
01 April 202426 April 2024 - Notification:
15 April 202403 May 2024 - Workshop: 1-3 July 2024
Sponsors
