5 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…