129 citations · 210 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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.…
Online learning with Erdős-Rényi side-observation graphs
Tomáš Kocák, Gergely Neu, Michal Valko
We consider adversarial multi-armed bandit problems where the learner is allowed to observe losses of a number of arms beside the arm that it actually chose. We study the case wher…
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…
Spectral bandits for smooth graph functions
Michal Valko, Rémi Munos, Branislav Kveton +1
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…
Online learning with noisy side observations
Tomáš Kocák, Gergely Neu, Michal Valko
We propose a new partial-observability model for online learning problems where the learner, besides its own loss, also observes some noisy feedback about the other actions, depend…
Spectral Thompson sampling
Tomas Kocak, Michal Valko, Remi Munos +1
Thompson Sampling (TS) has attracted a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its p…