ICML 2021spotlight15 citations
Bias-Robust Bayesian Optimization via Dueling Bandits
Johannes Kirschner, Andreas Krause
Abstract
We consider Bayesian optimization in settings where observations can be adversarially biased, for example by an uncontrolled hidden confounder. Our first contribution is a reduction of the confounded setting to the dueling bandit model. Then we propose a novel approach for dueling bandits based on information-directed sampling (IDS). Thereby, we obtain the first efficient kernelized algorithm for dueling bandits that comes with cumulative regret guarantees. Our analysis further generalizes a previously proposed semi-parametric linear bandit model to non-linear reward functions, and uncovers interesting links to doubly-robust estimation.
BibTeX
@InProceedings{pmlr-v139-kirschner21a,
title = {Bias-Robust Bayesian Optimization via Dueling Bandits},
author = {Kirschner, Johannes and Krause, Andreas},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {5595--5605},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/kirschner21a/kirschner21a.pdf},
url = {https://proceedings.mlr.press/v139/kirschner21a.html},
abstract = {We consider Bayesian optimization in settings where observations can be adversarially biased, for example by an uncontrolled hidden confounder. Our first contribution is a reduction of the confounded setting to the dueling bandit model. Then we propose a novel approach for dueling bandits based on information-directed sampling (IDS). Thereby, we obtain the first efficient kernelized algorithm for dueling bandits that comes with cumulative regret guarantees. Our analysis further generalizes a previously proposed semi-parametric linear bandit model to non-linear reward functions, and uncovers interesting links to doubly-robust estimation.}
}