1 citations · 2 across the 6 of their papers we have counts for
3 papers · 1 filter
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
Côme Fiegel, Pierre Ménard, Tadashi Kozuno +2
We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback. Specifically, we focus on developing uncoupled algorithms that guarantee, without…
Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier
Come Fiegel, Pierre Menard, Tadashi Kozuno +2
We study the problem of learning minimax policies in zero-sum matrix games. Fiegel et al. (2025) recently showed that achieving last-iterate convergence in this setting is harder w…
Learning to Allocate Resources with Censored Feedback
Giovanni Montanari, Côme Fiegel, Corentin Pla +2
We study an online resource allocation problem in which at each round, a budget must be allocated across arms under censored feedback. An arm yields a reward if and only if…