30 citations · 78 across the 19 of their papers we have counts for
21 papers
On the Complexity of Preference-Based Bandits
Ahmed Ben Yahmed, Marc Abeille, Clément Calauzènes
We study preference-based bandits with general reward function classes, where a learner sequentially selects pairs of arms and observes binary preference feedback governed by the B…
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…
Variance-sensitive Thompson sampling for generalised linear bandits, revisited
Tom Perneczky, Marc Abeille, David Janz
We prove a variance-sensitive regret bound for Thompson sampling in stochastic generalised linear bandits. The argument assumes a warm-up, after which the regret is controlled thro…
Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
Ahmed Ben Yahmed, Hafedh El Ferchichi, Marc Abeille +1
We examine a multi-armed bandit problem with contextual information, where the objective is to ensure that each arm receives a minimum aggregated reward across contexts while simul…
On the Hardness of Reinforcement Learning with Transition Look-Ahead
Corentin Pla, Hugo Richard, Marc Abeille +2
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 decidi…