5 citations · 9 across the 6 of their papers we have counts for
11 papers
Differentially Private Deep Learning with ModelMix
Hanshen Xiao, Jun Wan, Srinivas Devadas
Training large neural networks with meaningful/usable differential privacy security guarantees is a demanding challenge. In this paper, we tackle this problem by revisiting the two…
On the Foundation of Sparse Sensing (Part II): Diophantine Sampling and Array Configuration
Hanshen Xiao, Beining Zhou, Guoqiang Xiao
In the second part of the series papers, we set out to study the algorithmic efficiency of sparse sensing. Stemmed from co-prime sensing, we propose a generalized framework, termed…
On the Foundation of Sparse Sensing (Part I): Necessary and Sufficient Sampling Theory and Robust Remaindering Problem
Hanshen Xiao, Yaowen Zhang, Guoqiang Xiao
In the first part of the series papers, we set out to answer the following question: given specific restrictions on a set of samplers, what kind of signal can be uniquely represent…
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…
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Di Wang, Hanshen Xiao, Srini Devadas +1
In this paper, we consider the problem of designing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such d…
Statistical Robust Chinese Remainder Theorem for Multiple Numbers
Hanshen Xiao, Nan Du, Zhikang T. Wang +1
Generalized Chinese Remainder Theorem (CRT) is a well-known approach to solve ambiguity resolution related problems. In this paper, we study the robust CRT reconstruction for multi…