8 citations · 16 across the 8 of their papers we have counts for
8 papers
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms
Dorian Baudry, Kazuya Suzuki, Junya Honda
In this paper we propose a general methodology to derive regret bounds for randomized multi-armed bandit algorithms. It consists in checking a set of sufficient conditions on the s…
Towards an efficient and risk aware strategy for guiding farmers in identifying best crop management
Romain Gautron, Dorian Baudry, Myriam Adam +2
Identification of best performing fertilizer practices among a set of contrasting practices with field trials is challenging as crop losses are costly for farmers. To identify best…
Top Two Algorithms Revisited
Marc Jourdan, Rémy Degenne, Dorian Baudry +2
Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms. They select t…
Efficient Algorithms for Extreme Bandits
Dorian Baudry, Yoan Russac, Emilie Kaufmann
In this paper, we contribute to the Extreme Bandit problem, a variant of Multi-Armed Bandits in which the learner seeks to collect the largest possible reward. We first study the c…
From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits
Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard
The stochastic multi-arm bandit problem has been extensively studied under standard assumptions on the arm's distribution (e.g bounded with known support, exponential family, etc).…
On Limited-Memory Subsampling Strategies for Bandits
Dorian Baudry, Yoan Russac, Olivier Cappé
There has been a recent surge of interest in nonparametric bandit algorithms based on subsampling. One drawback however of these approaches is the additional complexity required by…
Optimal Thompson Sampling strategies for support-aware CVaR bandits
Dorian Baudry, Romain Gautron, Emilie Kaufmann +1
In this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level alpha of the reward distributio…
Sub-sampling for Efficient Non-Parametric Bandit Exploration
Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard
In this paper we propose the first multi-armed bandit algorithm based on re-sampling that achieves asymptotically optimal regret simultaneously for different families of arms (name…