21 citations · 79 across the 19 of their papers we have counts for
19 papers
Truthful High Dimensional Sparse Linear Regression
Liyang Zhu, Amina Manseur, Meng Ding +3
We study the problem of fitting the high dimensional sparse linear regression model with sub-Gaussian covariates and responses, where the data are provided by strategic or self-int…
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 Stability and Generalization of Bilevel Optimization Problem
Meng Ding, Mingxi Lei, Yunwen Lei +2
(Stochastic) bilevel optimization is a frequently encountered problem in machine learning with a wide range of applications such as meta-learning, hyper-parameter optimization, and…
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…
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
Di Wang, Jiahao Ding, Lijie Hu +3
(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing th…
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness
Tianhang Zheng, Di Wang, Baochun Li +1
As a certified defensive technique, randomized smoothing has received considerable attention due to its scalability to large datasets and neural networks. However, several importan…
Facility Location Problem in Differential Privacy Model Revisited
Yunus Esencayi, Marco Gaboardi, Shi Li +1
In this paper we study the uncapacitated facility location problem in the model of differential privacy (DP) with uniform facility cost. Specifically, we first show that, under the…
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
Di Wang, Lijie Hu, Huanyu Zhang +2
In this paper, we study the problem of estimating smooth Generalized Linear Models (GLMs) in the Non-interactive Local Differential Privacy (NLDP) model. Different from its classic…
Differentially Private High Dimensional Sparse Covariance Matrix Estimation
Di Wang, Jinhui Xu
In this paper, we study the problem of estimating the covariance matrix under differential privacy, where the underlying covariance matrix is assumed to be sparse and of high dimen…
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
Di Wang, Adam Smith, Jinhui Xu
Minimizing a convex risk function is the main step in many basic learning algorithms. We study protocols for convex optimization which provably leak very little about the individua…
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Di Wang, Minwei Ye, Jinhui Xu
In this paper we study the differentially private Empirical Risk Minimization (ERM) problem in different settings. For smooth (strongly) convex loss function with or without (non)-…
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning
Di Wang, Jinhui Xu
In this paper, we revisit the large-scale constrained linear regression problem and propose faster methods based on some recent developments in sketching and optimization. Our algo…
Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case
Di Wang, Marco Gaboardi, Jinhui Xu
In this paper, we study the Empirical Risk Minimization problem in the non-interactive local model of differential privacy. In the case of constant or low dimensionality ()…