2 citations · 5 across the 11 of their papers we have counts for
5 papers · 1 filter
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
Imad Aouali, Victor-Emmanuel Brunel, David Rohde +1
Off-policy learning (OPL) often involves minimizing a risk estimator based on importance weighting to correct bias from the logging policy used to collect data. However, this metho…
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
Otmane Sakhi, Imad Aouali, Pierre Alquier +1
This work investigates the offline formulation of the contextual bandit problem, where the goal is to leverage past interactions collected under a behavior policy to evaluate, sele…
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Imad Aouali, Victor-Emmanuel Brunel, David Rohde +1
In interactive systems, actions are often correlated, presenting an opportunity for more sample-efficient off-policy evaluation (OPE) and learning (OPL) in large action spaces. We…
Diffusion Models Meet Contextual Bandits
Imad Aouali
Efficient online decision-making in contextual bandits is challenging, as methods without informative priors often suffer from computational or statistical inefficiencies. In this…
Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits
Nicolas Nguyen, Imad Aouali, András György +1
We study the problem of Bayesian fixed-budget best-arm identification (BAI) in structured bandits. We propose an algorithm that uses fixed allocations based on the prior informatio…