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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.
Bias-Robust Bayesian Optimization via Dueling Bandits J. Kirschner, A. KrauseIn Proc. International Conference on Machine Learning (ICML), 2021
Bibtex Entry:
	author = {Johannes Kirschner and Andreas Krause},
	booktitle = {Proc. International Conference on Machine Learning (ICML)},
	month = {July},
	pdf = {},
	title = {Bias-Robust Bayesian Optimization via Dueling Bandits},
	year = {2021}}