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cs.LG2025
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…
cs.LG2024
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 […
cs.LG2021
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…