Publications Publications Overview2022Conferences:Faster Rates of Differentially Private Stochastic Convex Optimization. Jinyan Su★, Lijie Hu★ and Di Wang. The 33rd International Conference on Algorithmic Learning Theory (ALT 2022). Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. [Link] Youming Tao*★, Yulian Wu*★, Peng Zhao and Di Wang. (* equal contribution) The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022). Selected as an Oral paper (Acceptance Rate: 44/1685=2.6%). On Facility Location Problem in Local Differential Privacy Model. Yunus Esencayi, Chenglin Fan, Di Wang, Marco Gaboradi, Vincent Cohen-Addad and Shi Li. The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022). High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data. [Link] Lijie Hu★, Shuo Ni★, Hanshen Xiao and Di Wang. To appear in The 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2022)2021Journals:Inferring Ground Truth From Crowdsourced Data Under Local Attribute Differential Privacy. [Link] Di Wang and Jinhui Xu. Theoretical Computer Science Volume 865, 14 April 2021, Pages 85-98. Differentially Private High Dimensional Sparse Covariance Matrix Estimation. [Link] Di Wang and Jinhui Xu. Theoretical Computer Science Volume 865, 14 April 2021, Pages 119-130. On Sparse Linear Regression in the Local Differential Privacy Model. [Link] Di Wang and Jinhui Xu. IEEE Transactions on Information Theory, Volume 67, no. 2, Pages 1182-1200, Feb. 2021.Conferences:Differentially Private Pairwise Learning Revisited. Zhiyu Xue*★, Shaoyang Yang*★, Mengdi Huai and Di Wang. (* equal contribution). The 30th International Joint Conference on Artificial Intelligence (IJCAI 2021). Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data. [Link] Di Wang*, Huanyu Zhang*, Marco Gaboardi and Jinhui Xu (* equal contribution). The 32nd International Conference on Algorithmic Learning Theory (ALT 2021).2020Journals:Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. [Link] Di Wang, Marco Gaboardi, Adam Smith and Jinhui Xu. Journal of Machine Learning Research, Volume 21, 200 (2020), Pages 1-39. Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding. [Link] Di Wang*, Xiangyu Guo*, Shi Li and Jinhui Xu (* equal contribution). Machine Learning, 109, 2283-2311 (2020). Tight Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation. [Link] Di Wang and Jinhui Xu. Theoretical Computer Science, Volume 815, 2 May 2020, Pages 47-59. Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably. [Link] Di Wang* , Xiangyu Guo* , Chaowen Guan, Shi Li and Jinhui Xu (* equal contribution). Neurocomputing, Volume 399, 25 July 2020, Pages 129-140. Principal Component Analysis in the Local Differential Privacy Model. [Link] Di Wang and Jinhui Xu. Theoretical Computer Science, Volume 809, 24 February 2020, Pages 296-312.Conferences:Global Interpretation for Patient Similarity Learning. [Link] Mengdi Huai, Chenglin Miao, Jinduo Liu, Di Wang, Jingyuan Chou and Aidong Zhang. The 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020). Selected as Regular Paper (Acceptance Rate: 19.4%). Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method. [Link] Di Wang and Jinhui Xu. The 2020 European Conference on Machine Learning (ECML-PKDD 2020). On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data. [Link] Di Wang*, Hanshen Xiao*, Srini Devadas and Jinhui Xu (* equal contribution). The 37th International Conference on Machine Learning (ICML 2020). Scalable Estimating Stochastic Linear Combination of Non-linear Regressions. [Link] Di Wang* , Xiangyu Guo*, Chaowen Guan, Shi Li and Jinhui Xu (* equal contribution). The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). Pairwise Learning with Differential Privacy Guarantees. [Link] Mengdi Huai*, Di Wang*, Chenglin Miao, Jinhui Xu and Aidong Zhang (* equal contribution). Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). Towards Interpretation of Pairwise Learning. [Link] Mengdi Huai, Di Wang, Chenglin Miao and Aidong Zhang. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).2019Journals:Faster Large Scale Constrained Linear Regression via Two-Step Preconditioning. [Link] Di Wang and Jinhui Xu. Neurocomputing, Volume 364, 28 October 2019, Pages 280-296.Conferences:Facility Location Problem in Differential Privacy Model Revisited. [Link] [alphabetic order] Yunus Esencayi, Marco Gaboardi, Shi Li and Di Wang Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2019. Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation. [Link] Di Wang and Jinhui Xu. The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). Principal Component Analysis in the Local Differential Privacy Model. [Link] Di Wang and Jinhui Xu . The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). Privacy-aware Synthesizing for Crowdsourced Data. [Link] Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang. The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). Differentially Private Empirical Risk Minimization with Non-convex Loss Functions. [Link] Di Wang, Changyou Chen and Jinhui Xu. The 36th International Conference on Machine Learning (ICML 2019). On Sparse Linear Regression in the Local Differential Privacy Model. [Link] Di Wang and Jinhui Xu. The 36th International Conference on Machine Learning (ICML 2019). Selected as Long Talk(Acceptance Rate: 140/3424= 4.1%) . Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding. [Link] Di Wang, Jinhui Xu and Yang He. The 53rd Annual Conference on Information Sciences and Systems (CISS 2019). Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations. [Link] Di Wang, Adam Smith and Jinhui Xu. The 30th International Conference on Algorithmic Learning Theory (ALT 2019). Differentially Private Empirical Risk Minimization with Smooth Non-convex Loss Functions: A Non-stationary View. [Link] Di Wang and Jinhui Xu. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019). Selected as Oral Presentation (Acceptance Rate: 460/7095=6.5%).2018Conferences:Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited. [Link] Di Wang, Marco Gaboardi and Jinhui Xu. Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2018. Differentially Private Sparse Inverse Covariance Estimation. [Link] Di Wang, Mengdi Huai and Jinhui Xu. 2018 6th IEEE Global Conference on Signal and Information Processing (2018 GlobalSip). Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning. [Link] Di Wang and Jinhui Xu. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018).2017Conferences:Differentially Private Empirical Risk Minimization Revisited: Faster and More General. [Link] Di Wang, Minwei Ye and Jinhui Xu. Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2017.
