NeurIPS 2017poster27 citations
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Abstract
Existing strategies for finite-armed stochastic bandits mostly depend on a parameter of scale that must be known in advance. Sometimes this is in the form of a bound on the payoffs, or the knowledge of a variance or subgaussian parameter. The notable exceptions are the analysis of Gaussian bandits with unknown mean and variance by Cowan and Katehakis [2015a] and of uniform distributions with unknown support [Cowan and Katehakis, 2015b]. The results derived in these specialised cases are generalised here to the non-parametric setup, where the learner knows only a bound on the kurtosis of the noise, which is a scale free measure of the extremity of outliers.
BibTeX
@inproceedings{NIPS2017_fed33392,
author = {Lattimore, Tor},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/fed33392d3a48aa149a87a38b875ba4a-Paper.pdf},
volume = {30},
year = {2017}
}