28 citations · 93 across the 29 of their papers we have counts for
19 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…
Differentially Private Linear Bandits with Partial Distributed Feedback
Fengjiao Li, Xingyu Zhou, Bo Ji
In this paper, we study the problem of global reward maximization with only partial distributed feedback. This problem is motivated by several real-world applications (e.g., cellul…
Distributed Differential Privacy in Multi-Armed Bandits
Sayak Ray Chowdhury, Xingyu Zhou
We consider the standard -armed bandit problem under a distributed trust model of differential privacy (DP), which enables to guarantee privacy without a trustworthy server. Und…
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
Arnob Ghosh, Xingyu Zhou, Ness Shroff
We study the constrained reinforcement learning problem, in which an agent aims to maximize the expected cumulative reward subject to a constraint on the expected total value of a…
On Kernelized Multi-Armed Bandits with Constraints
Xingyu Zhou, Bo Ji
We study a stochastic bandit problem with a general unknown reward function and a general unknown constraint function. Both functions can be non-linear (even non-convex) and are as…
Shuffle Private Linear Contextual Bandits
Sayak Ray Chowdhury, Xingyu Zhou
Differential privacy (DP) has been recently introduced to linear contextual bandits to formally address the privacy concerns in its associated personalized services to participatin…
Differentially Private Reinforcement Learning with Linear Function Approximation
Xingyu Zhou
Motivated by the wide adoption of reinforcement learning (RL) in real-world personalized services, where users' sensitive and private information needs to be protected, we study re…
Differentially Private Regret Minimization in Episodic Markov Decision Processes
Sayak Ray Chowdhury, Xingyu Zhou
We study regret minimization in finite horizon tabular Markov decision processes (MDPs) under the constraints of differential privacy (DP). This is motivated by the widespread appl…
Wasserstein Generative Learning of Conditional Distribution
Shiao Liu, Xingyu Zhou, Yuling Jiao +1
Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a co…
Adaptive Control of Differentially Private Linear Quadratic Systems
Sayak Ray Chowdhury, Xingyu Zhou, Ness Shroff
In this paper, we study the problem of regret minimization in reinforcement learning (RL) under differential privacy constraints. This work is motivated by the wide range of RL app…
Weighted Gaussian Process Bandits for Non-stationary Environments
Yuntian Deng, Xingyu Zhou, Baekjin Kim +3
In this paper, we consider the Gaussian process (GP) bandit optimization problem in a non-stationary environment. To capture external changes, the black-box function is allowed to…
No-Regret Algorithms for Time-Varying Bayesian Optimization
Xingyu Zhou, Ness Shroff
In this paper, we consider the time-varying Bayesian optimization problem. The unknown function at each time is assumed to lie in an RKHS (reproducing kernel Hilbert space) with a…
Local Differential Privacy for Bayesian Optimization
Xingyu Zhou, Jian Tan
Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process set…
Multi-Armed Bandits with Local Differential Privacy
Wenbo Ren, Xingyu Zhou, Jia Liu +1
This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. In stochastic bandit systems, the…