4 citations · 8 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
Yoan Russac, Louis Faury, Olivier Cappé +1
Contextual sequential decision problems with categorical or numerical observations are ubiquitous and Generalized Linear Bandits (GLB) offer a solid theoretical framework to addres…
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…
Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces
Louis Faury, Flavian Vasile
Learning to optimize - the idea that we can learn from data algorithms that optimize a numerical criterion - has recently been at the heart of a growing number of research efforts.…