no code implementations • 22 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.
no code implementations • 15 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.
no code implementations • 8 Feb 2024 • Nicolas Nguyen, Imad Aouali, András György, Claire Vernade
We study the problem of Bayesian fixed-budget best-arm identification (BAI) in structured bandits.
no code implementations • 25 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.
no code implementations • 18 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.
no code implementations • 10 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.
1 code implementation • 30 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.
no code implementations • 26 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.