6 citations · 14 across the 11 of their papers we have counts for
11 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…
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…
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery
Junren Chen, Michael K. Ng, Di Wang
This paper studies the quantization of heavy-tailed data in some fundamental statistical estimation problems, where the underlying distributions have bounded moments of some order.…
Truthful Generalized Linear Models
Yuan Qiu, Jinyan Liu, Di Wang
In this paper we study estimating Generalized Linear Models (GLMs) in the case where the agents (individuals) are strategic or self-interested and they concern about their privacy…
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization
Junren Chen, Cheng-Long Wang, Michael K. Ng +1
In this paper, we propose a uniformly dithered 1-bit quantization scheme for high-dimensional statistical estimation. The scheme contains truncation, dithering, and quantization as…
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
Lijie Hu, Shuo Ni, Hanshen Xiao +1
As one of the most fundamental problems in machine learning, statistics and differential privacy, Differentially Private Stochastic Convex Optimization (DP-SCO) has been extensivel…
Faster Rates of Private Stochastic Convex Optimization
Jinyan Su, Lijie Hu, Di Wang
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) and provide excess population risks for some special classes of functions th…
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…