Bias-Robust Bayesian Optimization via Dueling Bandits
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.
Cite
Text
Kirschner and Krause. "Bias-Robust Bayesian Optimization via Dueling Bandits." International Conference on Machine Learning, 2021.Markdown
[Kirschner and Krause. "Bias-Robust Bayesian Optimization via Dueling Bandits." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/kirschner2021icml-biasrobust/)BibTeX
@inproceedings{kirschner2021icml-biasrobust,
title = {{Bias-Robust Bayesian Optimization via Dueling Bandits}},
author = {Kirschner, Johannes and Krause, Andreas},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {5595-5605},
volume = {139},
url = {https://mlanthology.org/icml/2021/kirschner2021icml-biasrobust/}
}