2021-03-18-dubey21a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
No-Regret Algorithms for Private Gaussian Process Bandit Optimization	The widespread proliferation of data-driven decision-making has ushered in a recent interest in the design of privacy-preserving algorithms. In this paper, we consider the ubiquitous problem of gaussian process (GP) bandit optimization from the lens of privacy-preserving statistics. We propose a solution for differentially private GP bandit optimization that combines uniform kernel approximation with random perturbations, providing a generic framework to create differentially-private (DP) Gaussian process bandit algorithms. For two specific DP settings - joint and local differential privacy, we provide algorithms based on efficient quadrature Fourier feature approximators, that are computationally efficient and provably no-regret for a class of stationary kernel functions. In contrast to previous work, our algorithms maintain differential privacy throughout the optimization procedure and critically do not rely on the sample path for prediction, making them scalable and straightforward to release as well.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	dubey21a	0	No-Regret Algorithms for Private Gaussian Process Bandit Optimization	2062	2070	2062-2070	2062	false	Dubey, Abhimanyu	given family Abhimanyu Dubey	2021-03-18		Proceedings of The 24th International Conference on Artificial Intelligence and Statistics	130	inproceedings	date-parts 2021 3 18	http://proceedings.mlr.press/v130/dubey21a/dubey21a.pdf	label link Supplementary PDF http://proceedings.mlr.press/v130/dubey21a/dubey21a-supp.pdf