129 citations · 210 across the 8 of their papers we have counts for
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cs.LG2026★ 129 cited
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…
cs.LG2026★ 26 cited
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…
cs.LG2026
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…