6 citations · 14 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited
Jinyan Su, Changhong Zhao, Di Wang
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general spaces. Specifically, we focus on three…
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…