no code implementations • 27 Nov 2018 • Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko
In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be stationary.
no code implementations • 25 Nov 2017 • Andrea Locatelli, Alexandra Carpentier, Samory Kpotufe
The problem of adaptivity (to unknown distributional parameters) has remained opened since the seminal work of Castro and Nowak (2007), which first established (active learning) rates for this setting.
no code implementations • 16 Mar 2017 • Andrea Locatelli, Alexandra Carpentier, Samory Kpotufe
This work addresses various open questions in the theory of active learning for nonparametric classification.
no code implementations • 29 May 2016 • Alexandra Carpentier, Andrea Locatelli
We consider the problem of \textit{best arm identification} with a \textit{fixed budget $T$}, in the $K$-armed stochastic bandit setting, with arms distribution defined on $[0, 1]$.
no code implementations • 27 May 2016 • Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier
We study a specific \textit{combinatorial pure exploration stochastic bandit problem} where the learner aims at finding the set of arms whose means are above a given threshold, up to a given precision, and \textit{for a fixed time horizon}.