10 papers
Spectral bandits for smooth graph functions with applications in recommender systems
Tomáš Kocák, Michal Valko, Rémi Munos +2
Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph…
Bandits attack function optimization
Philippe Preux, Rémi Munos, Michal Valko
We consider function optimization as a sequential decision making problem under budget constraint. This constraint limits the number of objective function evaluations allowed durin…
Black-box optimization of noisy functions with unknown smoothness
Jean-Bastien Grill, Michal Valko, Rémi Munos
We study the problem of black-box optimization of a function f of any dimension, given function evaluations perturbed by noise. The function is assumed to be locally smooth around…
Spectral bandits
Tomáš Kocák, Rémi Munos, Branislav Kveton +2
Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this work, we study a bandit problem where the payoffs of arms are smooth on a graph.…
Efficient learning by implicit exploration in bandit problems with side observations
Tomas Kocak, Gergely Neu, Michal Valko +1
We consider online learning problems under a partial observability model capturing situations where the information conveyed to the learner is between full information and bandit f…
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…