Di Wang

发表

Differentially Private 𝓁1-norm Linear Regression with Heavy-tailed Data

Di Wang, Jinhui Xu, 2022, ArXiv.

Supplemental Material For " Differenctially Private Empirical Risk Minimization with Non-convex Loss Functions "

Changyou Chen, Jinhui Xu, Di Wang, 2019 .

Differentially Private Empirical Risk Minimization with Non-convex Loss Functions

Di Wang, Changyou Chen, Jinhui Xu, 2019, ICML.

On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data

Jinyan Su, Di Wang, Jinhui Xu, 2022, ArXiv.

On Facility Location Problem in the Local Differential Privacy Model

Marco Gaboardi, Chenglin Fan, Di Wang, 2022, AISTATS.

Estimating stochastic linear combination of non-linear regressions efficiently and scalably

Shi Li, Jinhui Xu, Chaowen Guan, 2020, Neurocomputing.

Estimating Stochastic Linear Combination of Non-Linear Regressions

Jinhui Xu, Shi Li, Di Wang, 2020, AAAI.

Privacy-aware Synthesizing for Crowdsourced Data

Chenglin Miao, Aidong Zhang, Mengdi Huai, 2019, IJCAI.

Differentially Private Empirical Risk Minimization Revisited: Faster and More General pdf

Di Wang, Jinhui Xu, Minwei Ye, 2018, NIPS.

Pairwise Learning with Differential Privacy Guarantees

Chenglin Miao, Aidong Zhang, Mengdi Huai, 2020, AAAI.

Robust high dimensional expectation maximization algorithm via trimmed hard thresholding

Shi Li, Di Wang, Jinhui Xu, 2020, Machine Learning.

On Sparse Linear Regression in the Local Differential Privacy Model

Di Wang, Jinhui Xu, Jinhui Xu, 2019, IEEE Transactions on Information Theory.

Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited

Di Wang, Marco Gaboardi, Jinhui Xu, 2018, NeurIPS.

On Stability and Generalization of Bilevel Optimization Problem

Di Wang, Yunwen Lei, Jinhui Xu, 2022, ArXiv.

A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction

S. Devadas, Di Wang, Hanshen Xiao, 2023, 2023 IEEE Symposium on Security and Privacy (SP).

Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View

Di Wang, Jinhui Xu, Jinhui Xu, 2019, AAAI.

On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data

Jinhui Xu, Di Wang, Hanshen Xiao, 2020, ICML.

Principal Component Analysis in the Local Differential Privacy Model

Di Wang, Jinhui Xu, Jinhui Xu, 2019, IJCAI.

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy pdf

Di Wang, Marco Gaboardi, Jinhui Xu, 2020, J. Mach. Learn. Res..

DIFFERENTIALLY PRIVATE SPARSE INVERSE COVARIANCE ESTIMATION

Mengdi Huai, Di Wang, Jinhui Xu, 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data pdf

Jinhui Xu, Huanyu Zhang, Marco Gaboardi, 2019, ALT.

Efficient Empirical Risk Minimization with Smooth Loss Functions in Non-interactive Local Differential Privacy pdf

Marco Gaboardi, Di Wang, Jinhui Xu, 2018, ArXiv.

Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited

Di Wang, Marco Gaboardi, Jinhui Xu, 2018, NeurIPS.

Differentially Private Empirical Risk Minimization Revisited: Faster and More General

Jinhui Xu, Di Wang, Minwei Ye, 2018, NIPS.

Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data

Huanyu Zhang, Di Wang, Lijie Hu, 2019, J. Mach. Learn. Res..

Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data

Marco Gaboardi, Jinhui Xu, Huanyu Zhang, 2019, ALT.