139 citations · 615 across the 107 of their papers we have counts for
26 papers · 1 filter
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…
Minimax PAC Bounds for Learning in Exogenous Contextual MDPs
Corentin Pla, Hugo Richard, Marc Abeille +1
We introduce a PAC framework in which the learner can access sampling oracles both before and at decision time. Sample complexity is measured by a pair , where is the le…
Instance-dependent Stochastic Lipschitz bandit
Marius Potfer, Vianney Perchet
We study the Lipschitz bandit problem, where a learner sequentially maximizes an unknown Lipschitz function over a domain using noisy pointwise ev…
Do Not Trust The Auctioneer: Learning to Bid in Feedback-Manipulated Auctions
Luigi Foscari, Matilde Tullii, Vianney Perchet
Shilling is the use of artificial bids to make competition appear stronger and push prices upward. We study repeated first-price auctions in which shilling affects feedback but not…
Covariance-adapting algorithm for semi-bandits with application to sparse rewards
Pierre Perrault, Vianney Perchet, Michal Valko
We investigate stochastic combinatorial semi-bandits, where the entire joint distribution of outcomes impacts the complexity of the problem instance (unlike in the standard bandits…
Learning in Prophet Inequalities with Noisy Observations
Jung-hun Kim, Vianney Perchet
We study the prophet inequality, a fundamental problem in online decision-making and optimal stopping, in a practical setting where rewards are observed only through noisy realizat…