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 a uniform kernel approximator 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 popular stationary kernel functions. Our algorithms maintain differential privacy throughout the optimization procedure and critically do not rely explicitly on the sample path for prediction, making the parameters straightforward to release as well.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingPrivacy PreservingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret
We address differentially private stochastic bandit problems from the angles of exploring the deep connections among Thompson Sampling with Gaussian priors, Gaussian mechanisms, and Gaussian differential privacy (GDP). W…
Thompson SamplingDifferentially Private Contextual Linear Bandits
We study the contextual linear bandit problem, a version of the standard stochastic multi-armed bandit (MAB) problem where a learner sequentially selects actions to maximize a reward which depends also on a user provided…
Differentially Private Stochastic Linear Bandits: (Almost) for Free
In this paper, we propose differentially private algorithms for the problem of stochastic linear bandits in the central, local and shuffled models. In the central model, we achieve almost the same regret as the optimal n…
Concentrated Differential Privacy for Bandits
Bandits serve as the theoretical foundation of sequential learning and an algorithmic foundation of modern recommender systems. However, recommender systems often rely on user-sensitive data, making privacy a critical co…
Multi-Armed BanditsRecommendation Systems(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings
We provide a general technique for making online learning algorithms differentially private, in both the full information and bandit settings. Our technique applies to algorithms that aim to minimize a \emph{convex} loss…