21 citations · 66 across the 23 of their papers we have counts for
21 papers · 1 filter
Understanding Private Learning From Feature Perspective
Meng Ding, Mingxi Lei, Shaopeng Fu +3
Differentially private Stochastic Gradient Descent (DP-SGD) has become integral to privacy-preserving machine learning, ensuring robust privacy guarantees in sensitive domains. Des…
Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
Difei Xu, Meng Ding, Zihang Xiang +2
We study Stochastic Convex Optimization in the Differential Privacy model (DP-SCO). Unlike previous studies, here we assume the population risk function satisfies the Tsybakov Nois…
Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization
Ruijia Zhang, Mingxi Lei, Meng Ding +3
In this paper, we study the problem of (finite sum) minimax optimization in the Differential Privacy (DP) model. Unlike most of the previous studies on the (strongly) convex-concav…
Nearly Optimal Differentially Private ReLU Regression
Meng Ding, Mingxi Lei, Shaowei Wang +3
In this paper, we investigate one of the most fundamental nonconvex learning problems, ReLU regression, in the Differential Privacy (DP) model. Previous studies on private ReLU reg…
Towards User-level Private Reinforcement Learning with Human Feedback
Jiaming Zhang, Mingxi Lei, Meng Ding +5
Reinforcement Learning with Human Feedback (RLHF) has emerged as an influential technique, enabling the alignment of large language models (LLMs) with human preferences. Despite th…
Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective
Meng Ding, Rohan Sharma, Changyou Chen +2
Machine Unlearning has emerged as a significant area of research, focusing on `removing' specific subsets of data from a trained model. Fine-tuning (FT) methods have become one of…
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…
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…
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 ()…