1 citations · 2 across the 6 of their papers we have counts for
6 papers
Adaptive multi-fidelity optimization with fast learning rates
Come Fiegel, Victor Gabillon, Michal Valko
In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimizing a locally smooth function…
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…
Local and adaptive mirror descents in extensive-form games
Côme Fiegel, Pierre Ménard, Tadashi Kozuno +3
We study how to learn -optimal strategies in zero-sum imperfect information games (IIG) with trajectory feedback. In this setting, players update their policies sequentially bas…
Adapting to game trees in zero-sum imperfect information games
Côme Fiegel, Pierre Ménard, Tadashi Kozuno +3
Imperfect information games (IIG) are games in which each player only partially observes the current game state. We study how to learn -optimal strategies in a zero-sum IIG thro…