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- Alexandra Carpentier, Michal Valko
- ICML
- 2015

We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples onlyâ€¦ (More)

- Michal Valko, Alexandra Carpentier, RÃ©mi Munos
- ICML
- 2013

We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption on the function, namely that it is locallyâ€¦ (More)

In this paper, we study the problem of estimating uniformly well the mean values of several distributions given a finite budget of samples. If the variance of the distributions were known, one couldâ€¦ (More)

- Alexandra Carpentier, RÃ©mi Munos
- NIPS
- 2011

We consider the problem of stratified sampling for Monte-Carlo integration. We model this problem in a multi-armed bandit setting, where the arms represent the strata, and the goal is to estimate aâ€¦ (More)

We study a specific 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, andâ€¦ (More)

- Alexandra Carpentier, RÃ©mi Munos
- ALT
- 2012

We consider the problem of online stratified sampling for Monte Carlo integration of a function given a finite budget of n noisy evaluations to the function. More precisely we focus on the problem ofâ€¦ (More)

- Alexandra Carpentier, Michal Valko
- NIPS
- 2014

In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values. In this paper, we study an efficient way toâ€¦ (More)

- Alexandra Carpentier, RÃ©mi Munos
- AISTATS
- 2012

We consider a linear stochastic bandit problem where the dimension K of the unknown parameter Î¸ is larger than the sampling budget n. In such cases, it is in general impossible to derive sub-linearâ€¦ (More)

- Alexandra Carpentier, Andrea Locatelli
- COLT
- 2016

We consider the problem of best arm identification with a fixed budget T , in theK-armed stochastic bandit setting, with arms distribution defined on [0, 1]. We prove that any bandit strategy, for atâ€¦ (More)

We consider a two-sample hypothesis testing problem, where the distributions are defined on the space of undirected graphs, and one has access to only one observation from each model. A motivatingâ€¦ (More)