60 citations · 107 across the 20 of their papers we have counts for
20 papers
Fast Slate Policy Optimization: Going Beyond Plackett-Luce
Otmane Sakhi, David Rohde, Nicolas Chopin
An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technol…
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…
Learning from aggregated data with a maximum entropy model
Alexandre Gilotte, Ahmed Ben Yahmed, David Rohde
Aggregating a dataset, then injecting some noise, is a simple and common way to release differentially private data.However, aggregated data -- even without noise -- is not an appr…
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…
Fast Offline Policy Optimization for Large Scale Recommendation
Otmane Sakhi, David Rohde, Alexandre Gilotte
Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of thes…