53 citations · 123 across the 8 of their papers we have counts for
7 papers · 1 filter
(Locally) Differentially Private Combinatorial Semi-Bandits
Xiaoyu Chen, Kai Zheng, Zixin Zhou +3
In this paper, we study Combinatorial Semi-Bandits (CSB) that is an extension of classic Multi-Armed Bandits (MAB) under Differential Privacy (DP) and stronger Local Differential P…
Locally Differentially Private (Contextual) Bandits Learning
Kai Zheng, Tianle Cai, Weiran Huang +2
We study locally differentially private (LDP) bandits learning in this paper. First, we propose simple black-box reduction frameworks that can solve a large family of context-free…
Combinatorial Semi-Bandit in the Non-Stationary Environment
Wei Chen, Liwei Wang, Haoyu Zhao +1
In this paper, we investigate the non-stationary combinatorial semi-bandit problem, both in the switching case and in the dynamic case. In the general case where (a) the reward fun…
Efficient Online Portfolio with Logarithmic Regret
Haipeng Luo, Chen-Yu Wei, Kai Zheng
We study the decades-old problem of online portfolio management and propose the first algorithm with logarithmic regret that is not based on Cover's Universal Portfolio algorithm a…
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints
Wenlong Mou, Liwei Wang, Xiyu Zhai +1
Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterati…
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible
Kai Zheng, Wenlong Mou, Liwei Wang
Non-interactive Local Differential Privacy (LDP) requires data analysts to collect data from users through noisy channel at once. In this paper, we extend the frontiers of Non-inte…
Efficient Private ERM for Smooth Objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou +1
In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we…