activity
20102026
most citedBatched bandit problems

139 citations · 615 across the 107 of their papers we have counts for

collaborators

111 papers

cs.LG2026

Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits

Sacha Arroues-Paykin, Etienne Boursier, Vianney Perchet

Motivated by applications to cognitive radio networks, multi-player multi-armed bandits (MMAB) have attracted considerable attention in recent years. The asynchronous variant, in w…

stat.ML2026

Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead

Corentin Pla, Hugo Richard, Marc Abeille +1

We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of actions before deciding its…

cs.GT2026

Kernel Methods for Refined Prophet Inequalities

Patrick Loiseau, Mathieu Molina, Vianney Perchet +2

The single-selection prophet inequality is a canonical Bayesian online selection problem in which independent nonnegative values arrive sequentially and the decision-maker must irr…

cs.LG2026

Learning When to Automate: Queue Control in Human-AI Service Systems

Giovanni Montanari, Marco Scarsini, Vianney Perchet

We study a human-AI service system in which tasks arrive sequentially and are processed through a two-stage architecture: an automated chatbot followed, when necessary, by a human…

cs.GT2026

I.i.d. Prophet Inequalities with Discounted Rewards: As Hard as the Non-i.i.d. Case

Jung-hun Kim, Vianney Perchet

We study prophet inequalities with discounted rewards, where i.i.d. base rewards are multiplicatively discounted over time. Our main message is that even this structured and arbitr…

cs.LG2026

Asymptotically Optimal Learning for Parametric Prophet Inequalities

Jung-hun Kim, Anna Grebennikova, Vianney Perchet

We study learning in prophet inequalities with i.i.d. rewards drawn from an exponential-type parametric family with an unknown parameter , a class that includes exponential, Par…