| Year | Author | Title | Journal/Conference/Book | Category |
|---|---|---|---|---|
| Oct 28,2023 | Lijie Hu*, Zihang Xiang*, Jiabin Liu, and Di Wang | Nearly Optimal Rates of Privacy-preserving Sparse Generalized Eigenvalue Problem | IEEE Transactions on Knowledge and Data Engineering | Journal Paper |
| Oct 18,2023 | Di Wang and Jinhui Xu | Gradient Complexity and Non-stationary Views of Differentially Private Empirical Risk Minimization | Theoretical Computer Science | Journal Paper |
| Aug 14,2023 | Tao Guo, Song Guo, Junxiao Wang, Xueyang Tang, Wenchao Xu | PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models — Federated Learning in Age of Foundation Model | IEEE Transactions on Mobile Computing | Journal Paper |
| Jun 23,2023 | Wenfei Fan, Resul Tugay, Yaoshu Wang, Min Xie, Muhammad Asif Ali | Learning and Deducing Temporal Orders | Proceedings of the VLDB Endowment (VLDB 2023) | Journal Paper |
| May 28,2023 | Di Wang*, Lijie Hu*, Huanyu Zhang, Marco Gaboardi, Jinhui Xu | Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data | Journal of Machine Learning Research | Journal Paper |
| Apr 23,2023 | Junren Chen, Cheng-Long Wang, Michael Kwok Po NG, Di Wang | High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization | IEEE Transactions on Information Theory | Journal Paper |
| Apr 14,2021 | Di Wang, Jinhui Xu | Differentially Private High Dimensional Sparse Covariance Matrix Estimation | Theoretical Computer Science | Journal Paper |
| Apr 01,2021 | Di Wang, Jinhui Xu | Inferring Ground Truth From Crowdsourced Data Under Local Attribute Differential Privacy | Theoretical Computer Science | Journal Paper |
| Feb 01,2021 | Di Wang, Jinhui Xu | On Sparse Linear Regression in the Local Differential Privacy Model | IEEE Transactions on Information Theory | Journal Paper |
| Nov 01,2020 | Di Wang*, Xiangyu Guo*, Shi Li, Jinhui Xu | Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding | Machine Learning Journal | Journal Paper |
| Sep 01,2020 | Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu | Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy | Journal of Machine Learning Research | Journal Paper |
| Jul 01,2020 | Di Wang*, Xiangyu Guo*, Chaowen Guan, Shi Li, Jinhui Xu | Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably | Neurocomputing | Journal Paper |
| May 01,2020 | Di Wang, Jinhui Xu | Tight Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation | Theoretical Computer Science | Journal Paper |
| Feb 01,2020 | Di Wang, Jinhui Xu | Principal Component Analysis in the Local Differential Privacy Model | Theoretical Computer Science | Journal Paper |
| Oct 01,2019 | Di Wang, Jinhui Xu | Faster Large Scale Constrained Linear Regression via Two-Step Preconditioning | Neurocomputing | Journal Paper |
