137 citations · 208 across the 22 of their papers we have counts for
9 papers · 1 filter
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting
Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet
We consider the classical multi-armed bandit problem, but with strategic arms. In this context, each arm is characterized by a bounded support reward distribution and strategically…
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
Morgane Goibert, Clément Calauzènes, Ekhine Irurozki +1
As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed. Prefere…
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Kwang-Sung Jun +1
Logistic Bandits have recently undergone careful scrutiny by virtue of their combined theoretical and practical relevance. This research effort delivered statistically efficient al…
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Louis Faury, Yoan Russac, Marc Abeille +1
Generalized Linear Bandits (GLBs) are powerful extensions to the Linear Bandit (LB) setting, broadening the benefits of reward parametrization beyond linearity. In this paper we st…
Wasserstein Learning of Determinantal Point Processes
Lucas Anquetil, Mike Gartrell, Alain Rakotomamonjy +2
Determinantal point processes (DPPs) have received significant attention as an elegant probabilistic model for discrete subset selection. Most prior work on DPP learning focuses on…
Real-Time Optimisation for Online Learning in Auctions
Lorenzo Croissant, Marc Abeille, Clément Calauzènes
In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day. In this context, revenue maximisation via monopoly price learning is a h…
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
Marc Abeille, Louis Faury, Clément Calauzènes
Logistic Bandits have recently attracted substantial attention, by providing an uncluttered yet challenging framework for understanding the impact of non-linearity in parametrized…
Improved Optimistic Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Clément Calauzènes +1
The generalized linear bandit framework has attracted a lot of attention in recent years by extending the well-understood linear setting and allowing to model richer reward structu…
Bridging the gap between regret minimization and best arm identification, with application to A/B tests
Rémy Degenne, Thomas Nedelec, Clément Calauzènes +1
State of the art online learning procedures focus either on selecting the best alternative ("best arm identification") or on minimizing the cost (the "regret"). We merge these two…