3 citations · 8 across the 9 of their papers we have counts for
8 papers
Differentially Private Reward Estimation with Preference Feedback
Sayak Ray Chowdhury, Xingyu Zhou, Nagarajan Natarajan
Learning from preference-based feedback has recently gained considerable traction as a promising approach to align generative models with human interests. Instead of relying on num…
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 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 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…
Adversarial Attack via Dual-Stage Network Erosion
Yexin Duan, Junhua Zou, Xingyu Zhou +3
Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. Although existing attacks have achieved promising results, i…