177 citations · 551 across the 52 of their papers we have counts for
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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…
Leveraging priors on distribution functions for multi-arm bandits
Sumit Vashishtha, Odalric-Ambrym Maillard
We introduce Dirichlet Process Posterior Sampling (DPPS), a Bayesian non-parametric algorithm for multi-arm bandits based on Dirichlet Process (DP) priors. Like Thompson-sampling,…
How to Shrink Confidence Sets for Many Equivalent Discrete Distributions?
Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
We consider the situation when a learner faces a set of unknown discrete distributions defined over a common alphabet , and can build for each…
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
Shubhada Agrawal, Timothée Mathieu, Debabrota Basu +1
We investigate the regret-minimisation problem in a multi-armed bandit setting with arbitrary corruptions. Similar to the classical setup, the agent receives rewards generated inde…
Risk-aware linear bandits with convex loss
Patrick Saux, Odalric-Ambrym Maillard
In decision-making problems such as the multi-armed bandit, an agent learns sequentially by optimizing a certain feedback. While the mean reward criterion has been extensively stud…
From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits
Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard
The stochastic multi-arm bandit problem has been extensively studied under standard assumptions on the arm's distribution (e.g bounded with known support, exponential family, etc).…