2 citations · 5 across the 11 of their papers we have counts for
12 papers
On-Policy and Off-Policy Learning for Large Action Spaces
Imad Aouali
This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback. The main frame…
Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
Imad Aouali, Otmane Sakhi
Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits. Recent advances in OPL primarily optimize OPE estimato…
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…
Exponential Smoothing for Off-Policy Learning
Imad Aouali, Victor-Emmanuel Brunel, David Rohde +1
Off-policy learning (OPL) aims at finding improved policies from logged bandit data, often by minimizing the inverse propensity scoring (IPS) estimator of the risk. In this work, w…
Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation
Imad Aouali, Amine Benhalloum, Martin Bompaire +5
Both in academic and industry-based research, online evaluation methods are seen as the golden standard for interactive applications like recommendation systems. Naturally, the rea…
Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation
Imad Aouali, Achraf Ait Sidi Hammou, Otmane Sakhi +2
We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation. Our approach allows off-policy estimation of the reward in t…
Mixed-Effect Thompson Sampling
Imad Aouali, Branislav Kveton, Sumeet Katariya
A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In th…
Combining Reward and Rank Signals for Slate Recommendation
Imad Aouali, Sergey Ivanov, Mike Gartrell +4
We consider the problem of slate recommendation, where the recommender system presents a user with a collection or slate composed of K recommended items at once. If the user finds…