177 citations · 551 across the 52 of their papers we have counts for
55 papers
Information-Based Exploration via Random Features for Reinforcement Learning
Waris Radji, Odalric-Ambrym Maillard
Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical gua…
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
Udvas Das, Waris Radji, Debabrota Basu +1
We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized pref…
Pliable rejection sampling
Akram Erraqabi, Michal Valko, Alexandra Carpentier +1
Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods ei…
How Hard is it to Confuse a World Model?
Waris Radji, Odalric-Ambrym Maillard
In reinforcement learning (RL) theory, the concept of most confusing instances is central to establishing regret lower bounds, that is, the minimal exploration needed to solve a pr…
The Confusing Instance Principle for Online Linear Quadratic Control
Waris Radji, Odalric-Ambrym Maillard
We revisit the problem of controlling linear systems with quadratic cost under unknown dynamics with model-based reinforcement learning. Traditional methods like Optimism in the Fa…
Asymptotically Optimal Problem-Dependent Bandit Policies for Transfer Learning
Adrien Prevost, Timothee Mathieu, Odalric-Ambrym Maillard
We study the non-contextual multi-armed bandit problem in a transfer learning setting: before any pulls, the learner is given N'_k i.i.d. samples from each source distribution nu'_…