28 citations · 93 across the 29 of their papers we have counts for
6 papers · 1 filter
Understanding the Role of Feedback in Online Learning with Switching Costs
Duo Cheng, Xingyu Zhou, Bo Ji
In this paper, we study the role of feedback in online learning with switching costs. It has been shown that the minimax regret is under bandit feedback and…
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury +1
In this paper, we study the problem of (finite horizon tabular) Markov decision processes (MDPs) with heavy-tailed rewards under the constraint of differential privacy (DP). Compar…
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
Honghao Wei, Arnob Ghosh, Ness Shroff +2
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected…
On Private and Robust Bandits
Yulian Wu, Xingyu Zhou, Youming Tao +1
We study private and robust multi-armed bandits (MABs), where the agent receives Huber's contaminated heavy-tailed rewards and meanwhile needs to ensure differential privacy. We fi…
(Private) Kernelized Bandits with Distributed Biased Feedback
Fengjiao Li, Xingyu Zhou, Bo Ji
In this paper, we study kernelized bandits with distributed biased feedback. This problem is motivated by several real-world applications (such as dynamic pricing, cellular network…
On Differentially Private Federated Linear Contextual Bandits
Xingyu Zhou, Sayak Ray Chowdhury
We consider cross-silo federated linear contextual bandit (LCB) problem under differential privacy, where multiple silos (agents) interact with the local users and communicate via…