Search Results for author: Imad Aouali

Found 8 papers, 1 papers with code

Bayesian Off-Policy Evaluation and Learning for Large Action Spaces

no code implementations22 Feb 2024 Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

In this framework, we propose sDM, a generic Bayesian approach designed for OPE and OPL, grounded in both algorithmic and theoretical foundations.

Computational Efficiency Off-policy evaluation

Diffusion Models Meet Contextual Bandits with Large Action Spaces

no code implementations15 Feb 2024 Imad Aouali

Efficient exploration is a key challenge in contextual bandits due to the large size of their action space, where uninformed exploration can result in computational and statistical inefficiencies.

Efficient Exploration Multi-Armed Bandits +1

Exponential Smoothing for Off-Policy Learning

no code implementations25 May 2023 Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

In particular, it is also valid for standard IPS without making the assumption that the importance weights are bounded.

valid

Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation

no code implementations18 Sep 2022 Imad Aouali, Amine Benhalloum, Martin Bompaire, Benjamin Heymann, Olivier Jeunen, David Rohde, Otmane Sakhi, Flavian vasile

Naturally, the reason for this is that we can directly measure utility metrics that rely on interventions, being the recommendations that are being shown to users.

counterfactual Recommendation Systems

Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation

no code implementations10 Aug 2022 Imad Aouali, Achraf Ait Sidi Hammou, Sergey Ivanov, Otmane Sakhi, David Rohde, Flavian vasile

We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation.

Recommendation Systems

Mixed-Effect Thompson Sampling

1 code implementation30 May 2022 Imad Aouali, Branislav Kveton, Sumeet Katariya

The regret bound has two terms, one for learning the action parameters and the other for learning the shared effect parameters.

Thompson Sampling

Combining Reward and Rank Signals for Slate Recommendation

no code implementations26 Jul 2021 Imad Aouali, Sergey Ivanov, Mike Gartrell, David Rohde, Flavian vasile, Victor Zaytsev, Diego Legrand

In this paper, we formulate several Bayesian models that incorporate the reward signal (Reward model), the rank signal (Rank model), or both (Full model), for non-personalized slate recommendation.

Recommendation Systems

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