5 citations · 6 across the 8 of their papers we have counts for
8 papers
Worst-Case Regret Bounds for Combinatorial Thompson Sampling in Sleeping Semi-Bandits
Zhiming Huang, Bingshan Hu, Jianping Pan
We revisit combinatorial Thompson sampling (CTS) for semi-bandits with sleeping arms, where arm availability varies over time and actions must satisfy combinatorial constraints, as…
Privacy Filters are Captured by Residues: A Characterization of Free Natural Filters and the Cost of Adaptivity
Matthew Regehr, Bingshan Hu, Ethan Leeman +3
We study privacy filters, which enable privacy accounting for differentially private (DP) mechanisms with adaptively chosen privacy characteristics. We develop a general theory tha…
Control Your Robot: A Unified System for Robot Control and Policy Deployment
Tian Nian, Weijie Ke, Shaolong Zhu +1
Cross-platform robot control remains difficult because hardware interfaces, data formats, and control paradigms vary widely, which fragments toolchains and slows deployment. To add…
Efficient kernelized bandit algorithms via exploration distributions
Bingshan Hu, Zheng He, Danica J. Sutherland
We consider a kernelized bandit problem with a compact arm set and a fixed but unknown reward function with a finite norm in some Reproducing Kern…
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret
Bingshan Hu, Zhiming Huang, Tianyue H. Zhang +2
We address differentially private stochastic bandit problems from the angles of exploring the deep connections among Thompson Sampling with Gaussian priors, Gaussian mechanisms, an…
Efficient and Adaptive Posterior Sampling Algorithms for Bandits
Bingshan Hu, Zhiming Huang, Tianyue H. Zhang +2
We study Thompson Sampling-based algorithms for stochastic bandits with bounded rewards. As the existing problem-dependent regret bound for Thompson Sampling with Gaussian priors […
Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment
Bingshan Hu, Zhiming Huang, Nishant A. Mehta +1
In this paper, we study differentially private online learning problems in a stochastic environment under both bandit and full information feedback. For differentially private stoc…
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints
Zhiming Huang, Yifan Xu, Bingshan Hu +2
We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F). To address the problem, we adopt Thompson Sampling~(TS) to maximi…