4 citations · 9 across the 6 of their papers we have counts for
13 papers
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data
Jinyan Su, Jinhui Xu, Di Wang
In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP). To breach the barrier of exponential sample complexit…
Differentially Private -norm Linear Regression with Heavy-tailed Data
Di Wang, Jinhui Xu
We study the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) with heavy-tailed data. Specifically, we focus on the -norm linear regression in the…
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
Di Wang, Marco Gaboardi, Adam Smith +1
In this paper, we study the Empirical Risk Minimization (ERM) problem in the non-interactive Local Differential Privacy (LDP) model. Previous research on this problem \citep{smith2…
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…
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding
Di Wang, Xiangyu Guo, Shi Li +1
In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i.e.,} ) where the underlying pa…
Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably
Di Wang, Xiangyu Guo, Chaowen Guan +2
Recently, many machine learning and statistical models such as non-linear regressions, the Single Index, Multi-index, Varying Coefficient Index Models and Two-layer Neural Networks…