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, 16 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).
Tutorials
- Simina Branzei (Purdue University, US)
- Sylvain Sorin (Sorbonne University, 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)
- Sam Jindani (NUS, Singapore)
- Maryam Kamgarpour (EPFL, Switzerland)
- Tran-Thanh Long (Univ. of Warwick, UK)
- Panayotis Mertikopoulous (CNRS, France)
- Mehryar Mohri (CIMS, US)
- Vianney Perchet (ENSAE, France)
- Marco Scarsini (Luiss University, Italy)
- Bassel Tarbush (Univ. of Oxford, 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
