79 citations · 394 across the 42 of their papers we have counts for
53 papers
Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension
Alexandra Carpentier, Chloé Rouyer, Alexandre Tsybakov +1
Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function evalu…
Active multiple matrix completion with adaptive confidence sets
Andrea Locatelli, Alexandra Carpentier, Michal Valko
In this work, we formulate a new multi-task active learning setting in which the learner's goal is to solve multiple matrix completion problems simultaneously. At each round, the l…
Revealing graph bandits for maximizing local influence
Alexandra Carpentier, Michal Valko
We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information from the graph as possible.…
Extreme bandits
Alexandra Carpentier, Michal Valko
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 eff…
Stochastic simultaneous optimistic optimization
Michal Valko, Alexandra Carpentier, Rémi Munos
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…
Pliable rejection sampling
Akram Erraqabi, Michal Valko, Alexandra Carpentier +1
Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods ei…