6 citations · 7 across the 5 of their papers we have counts for
5 papers
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…
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…
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning
Bhargav Ganguly, Yulian Wu, Di Wang +1
In this paper, we investigate the problem of \textit{episodic reinforcement learning} with quantum oracles for state evolution. To this end, we propose an \textit{Upper Confidence…
Quantum Heavy-tailed Bandits
Yulian Wu, Chaowen Guan, Vaneet Aggarwal +1
In this paper, we study multi-armed bandits (MAB) and stochastic linear bandits (SLB) with heavy-tailed rewards and quantum reward oracle. Unlike the previous work on quantum bandi…
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
Youming Tao, Yulian Wu, Peng Zhao +1
In this paper we investigate the problem of stochastic multi-armed bandits (MAB) in the (local) differential privacy (DP/LDP) model. Unlike previous results that assume bounded/sub…