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Found 2 papers, 1 papers with code
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise
In this study, we propose a new method for constructing UCB-type algorithms for stochastic multi-armed bandits based on general convex optimization methods with an inexact oracle.
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits
We establish convergence results under mild assumptions on the rewards distribution and demonstrate that INF-clip is optimal for linear heavy-tailed stochastic MAB problems and works well for non-linear ones.
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