Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards

Kyungjae Lee; Hongjun Yang; Sungbin Lim; Songhwai Oh

NeurIPS 2020poster35 citations

Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards

Kyungjae Lee, Hongjun Yang, Sungbin Lim, Songhwai Oh

Abstract

In this paper, we consider stochastic multi-armed bandits (MABs) with heavy-tailed rewards, whose p-th moment is bounded by a constant nu_p for 1

BibTeX

@inproceedings{NEURIPS2020_607bc9eb,
 author = {Lee, Kyungjae and Yang, Hongjun and Lim, Sungbin and Oh, Songhwai},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {8452--8462},
 publisher = {Curran Associates, Inc.},
 title = {Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/607bc9ebe4abfcd65181bfbef6252830-Paper.pdf},
 volume = {33},
 year = {2020}
}