ICML 2022spotlight8 citations
Asymptotically-Optimal Gaussian Bandits with Side Observations
Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai
Abstract
We study the problem of Gaussian bandits with general side information, as first introduced by Wu, Szepesvári, and György. In this setting, the play of an arm reveals information about other arms, according to an arbitrary
BibTeX
@InProceedings{pmlr-v162-atsidakou22a,
title = {Asymptotically-Optimal {G}aussian Bandits with Side Observations},
author = {Atsidakou, Alexia and Papadigenopoulos, Orestis and Caramanis, Constantine and Sanghavi, Sujay and Shakkottai, Sanjay},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {1057--1077},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/atsidakou22a/atsidakou22a.pdf},
url = {https://proceedings.mlr.press/v162/atsidakou22a.html},
abstract = {We study the problem of Gaussian bandits with general side information, as first introduced by Wu, Szepesvári, and György. In this setting, the play of an arm reveals information about other arms, according to an arbitrary