4 citations · 10 across the 9 of their papers we have counts for
13 papers · 1 filter
Understanding Forgetting in Continual Learning with Linear Regression
Meng Ding, Kaiyi Ji, Di Wang +1
Continual learning, focused on sequentially learning multiple tasks, has gained significant attention recently. Despite the tremendous progress made in the past, the theoretical un…
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
Liyang Zhu, Meng Ding, Vaneet Aggarwal +2
In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local mod…
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…