177 citations · 551 across the 52 of their papers we have counts for
6 papers · 1 filter
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation
Frédérick Fabre Ferber, Dominique Gay, Jean-Christophe Soulié +2
Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techni…
Power Mean Estimation in Stochastic Monte-Carlo Tree_Search
Tuan Dam, Odalric-Ambrym Maillard, Emilie Kaufmann
Monte-Carlo Tree Search (MCTS) is a widely-used strategy for online planning that combines Monte-Carlo sampling with forward tree search. Its success relies on the Upper Confidence…
Monte-Carlo tree search with uncertainty propagation via optimal transport
Tuan Dam, Pascal Stenger, Lukas Schneider +3
This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) designed for highly stochastic and partially observable Markov decision processes. We adopt a proba…
gym-DSSAT: a crop model turned into a Reinforcement Learning environment
Romain Gautron, Emilio J. Padrón, Philippe Preux +3
Addressing a real world sequential decision problem with Reinforcement Learning (RL) usually starts with the use of a simulated environment that mimics real conditions. We present…
Indexed Minimum Empirical Divergence for Unimodal Bandits
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
We consider a multi-armed bandit problem specified by a set of one-dimensional family exponential distributions endowed with a unimodal structure. We introduce IMED-UB, a algorithm…
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem
Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard
We consider a non-stationary formulation of the stochastic multi-armed bandit where the rewards are no longer assumed to be identically distributed. For the best-arm identification…