ICML 2022spotlight5 citations
Instance Dependent Regret Analysis of Kernelized Bandits
Shubhanshu Shekhar, Tara Javidi
Abstract
We study the problem of designing an adaptive strategy for querying a noisy zeroth-order-oracle to efficiently learn about the optimizer of an unknown function $f$. To make the problem tractable, we assume that $f$ lies in the reproducing kernel Hilbert space (RKHS) associated with a known kernel $K$, with its norm bounded by $M<\infty$. Prior results, working in a
BibTeX
@InProceedings{pmlr-v162-shekhar22a,
title = {Instance Dependent Regret Analysis of Kernelized Bandits},
author = {Shekhar, Shubhanshu and Javidi, Tara},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {19747--19772},
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/shekhar22a/shekhar22a.pdf},
url = {https://proceedings.mlr.press/v162/shekhar22a.html},
abstract = {We study the problem of designing an adaptive strategy for querying a noisy zeroth-order-oracle to efficiently learn about the optimizer of an unknown function $f$. To make the problem tractable, we assume that $f$ lies in the reproducing kernel Hilbert space (RKHS) associated with a known kernel $K$, with its norm bounded by $M<\infty$. Prior results, working in a