Di Wang's Publications
   


(★ my Master/PhD/Intern/Visiting students)

2023

    Conference Papers

  1. High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile Devices. Abstract
    Rui Chen, Qiyu Wan, Xinyue Zhang, Xiaoqi Qin, Di Wang, Xin Fu, and Miao Pan.
    The 21st ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2023).

  2. On Practical Differentially Private and Byzantine-resilient Federated Learning. Abstract
    Zihang Xiang, Tianhao Wang, Wanyu Lin, and Di Wang.
    International Conference on Management of Data (SIGMOD 2023).

  3. Privacy-preserving Sparse Generalized Eigenvalue Problem. Abstract
    Lijie Hu*, Zihang Xiang*, Jiabin Liu, and Di Wang. (* equal contribution)
    The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023).

  4. SEAT: Stable and Explainable Attention. Abstract
    Lijie Hu*, Yixin Liu *, Ninghao Liu , Mengdi Huai, Lichao Sun, and Di Wang. (* equal contribution)
    The 37th AAAI Conference on Artificial Intelligence (AAAI 2023).
    Selected as an Oral paper.

  5. Journal Papers

  6. High Dimensional Sparse Statistical Estimation Under One-bit Quantization. [Link] Abstract
    Junren Chen, Cheng-Long Wang, Michael Kwok Po NG, and Di Wang.
    Accepted at IEEE Transactions on Information Theory.

  7. Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data. [Link] Abstract
    Di Wang*, Lijie Hu*, Huanyu Zhang, Marco Gaboardi, and Jinhui Xu. (* equal contribution)
    Minor Revision, Journal of Machine Learning Research.

2022

    Conference Papers

  1. Truthful Generalized Linear Models. [Link] Abstract
    Yuan Qiu, Jinyan Liu, and Di Wang.
    The 18th Conference on Web and Internet Economics (WINE 2022)

  2. On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data. [Link] Abstract
    Jinyan Su, Jinhui Xu, and Di Wang.
    The 14th Asian Conference on Machine Learning (ACML 2022)
    Best Paper Award.

  3. Differentially Private $\ell_1$-norm Linear Regression with Heavy-tailed Data. [Link] Abstract
    Di Wang and Jinhui Xu.
    2022 IEEE International Symposium on Information Theory (ISIT 2022)

  4. Private Stochastic Convex Optimization and Sparse Learning with Heavy-tailed Data Revisited. [Link] Abstract
    Youming Tao, Yulian Wu, Xiuzhen Cheng, and Di Wang.
    The 31st International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2022).

  5. Faster Rates of Private Stochastic Convex Optimization. [Link] Abstract
    Jinyan Su, Lijie Hu, and Di Wang.
    The 33rd International Conference on Algorithmic Learning Theory (ALT 2022)

  6. Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. [Link] Abstract
    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%).
    ACM CCS 2021 Workshop on Privacy Preserving Machine Learning.
    ICML 2022 Workshop on Responsible Decision Making in Dynamic Environments (Selected as Contributed Talk).

  7. On Facility Location Problem in Local Differential Privacy Model. Abstract
    [alphabetic order] Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboradi, Shi Li, and Di Wang.
    The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022).

  8. High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data. [Link] Abstract
    Lijie Hu, Shuo Ni, Hanshen Xiao, and Di Wang.
    The 41st ACM Symposium on Principles of Database Systems (PODS 2022).
    Invited to The ACM Transactions on Database Systems special issue on Best of PODS 2022.
    ACM CCS 2021 Workshop on Privacy Preserving Machine Learning.

2021

    Conference Papers

  1. Differentially Private Pairwise Learning Revisited. Abstract
    Zhiyu Xue*, Shaoyang Yang*, Mengdi Huai and Di Wang. (* equal contribution)
    The 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).

  2. Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data [Link] Abstract
    Di Wang*, Huanyu Zhang*, Marco Gaboardi and Jinhui Xu. (* equal contribution)
    The 32nd International Conference on Algorithmic Learning Theory (ALT 2021).
    NeurIPS 2019 Workshop on Privacy in Machine Learning.
  3. Journal Papers

  4. Inferring Ground Truth From Crowdsourced Data Under Local Attribute Differential Privacy [Link] Abstract
    Di Wang and Jinhui Xu.
    Theoretical Computer Science Volume 865, 14 April 2021, Pages 85-98.

  5. Differentially Private High Dimensional Sparse Covariance Matrix Estimation [Link] Abstract
    Di Wang and Jinhui Xu.
    Theoretical Computer Science Volume 865, 14 April 2021, Pages 119-130.

  6. On Sparse Linear Regression in the Local Differential Privacy Model [Link] Abstract
    Di Wang and Jinhui Xu.
    IEEE Transactions on Information Theory, Volume 67, no. 2, Pages 1182-1200, Feb. 2021.

2020

    Conference Papers

  1. Global Interpretation for Patient Similarity Learning [Link] Abstract
    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%).

  2. Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method [Link] Abstract
    Di Wang and Jinhui Xu.
    The 2020 European Conference on Machine Learning (ECML-PKDD 2020).

  3. On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data [Link] Abstract
    Di Wang*, Hanshen Xiao*, Srini Devadas and Jinhui Xu (* equal contribution).
    The 37th International Conference on Machine Learning (ICML 2020).

  4. Scalable Estimating Stochastic Linear Combination of Non-linear Regressions [Link] Abstract
    Di Wang* , Xiangyu Guo*, Chaowen Guan, Shi Li and Jinhui Xu (* equal contribution).
    The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).

  5. Pairwise Learning with Differential Privacy Guarantees [Link] Abstract
    Mengdi Huai*, Di Wang*, Chenglin Miao, Jinhui Xu and Aidong Zhang (* equal contribution).
    Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).

  6. Towards Interpretation of Pairwise Learning [Link] Abstract
    Mengdi Huai, Di Wang, Chenglin Miao and Aidong Zhang.
    The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).
  7. Journal Papers

  8. Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. [Link] Abstract
    Di Wang, Marco Gaboardi, Adam Smith and Jinhui Xu.
    Journal of Machine Learning Research, Volume 21, 200 (2020), Pages 1-39.

  9. Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding. [Link] Abstract
    Di Wang*, Xiangyu Guo*, Shi Li and Jinhui Xu (* equal contribution).
    Machine Learning, 109, 2283-2311 (2020).

  10. Tight Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation. [Link] Abstract
    Di Wang and Jinhui Xu.
    Theoretical Computer Science, Volume 815, 2 May 2020, Pages 47-59.

  11. Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably. [Link] Abstract
    Di Wang* , Xiangyu Guo* , Chaowen Guan, Shi Li and Jinhui Xu (* equal contribution).
    Neurocomputing, Volume 399, 25 July 2020, Pages 129-140.

  12. Principal Component Analysis in the Local Differential Privacy Model. [Link] Abstract
    Di Wang and Jinhui Xu.
    Theoretical Computer Science, Volume 809, 24 February 2020, Pages 296-312.

2019

    Conference Papers

  1. Facility Location Problem in Differential Privacy Model Revisited. [Link] Abstract
    [alphabetic order] Yunus Esencayi, Marco Gaboardi, Shi Li and Di Wang
    Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2019.

  2. Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation. [Link] Abstract
    Di Wang and Jinhui Xu.
    The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019).

  3. Principal Component Analysis in the Local Differential Privacy Model. [Link] Abstract
    Di Wang and Jinhui Xu .
    The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019).

  4. Privacy-aware Synthesizing for Crowdsourced Data. [Link] Abstract
    Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang.
    The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019).

  5. Differentially Private Empirical Risk Minimization with Non-convex Loss Functions. [Link] Abstract
    Di Wang, Changyou Chen and Jinhui Xu.
    The 36th International Conference on Machine Learning (ICML 2019).

  6. On Sparse Linear Regression in the Local Differential Privacy Model. [Link] Abstract
    Di Wang and Jinhui Xu.
    The 36th International Conference on Machine Learning (ICML 2019).
    Selected as Long Talk (Acceptance Rate: 140/3424= 4.1%) .
    NeurIPS 2018 Workshop on Privacy Preserving Machine Learning.

  7. Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding. [Link] Abstract
    Di Wang, Jinhui Xu and Yang He.
    The 53rd Annual Conference on Information Sciences and Systems (CISS 2019).

  8. Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations. [Link] Abstract
    Di Wang, Adam Smith and Jinhui Xu.
    The 30th International Conference on Algorithmic Learning Theory (ALT 2019).

  9. Differentially Private Empirical Risk Minimization with Smooth Non-convex Loss Functions: A Non-stationary View. [Link] Abstract
    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%).
  10. Journal Papers

  11. Faster Large Scale Constrained Linear Regression via Two-Step Preconditioning. [Link] Abstract
    Di Wang and Jinhui Xu.
    Neurocomputing, Volume 364, 28 October 2019, Pages 280-296.

2018

    Conference Papers

  1. Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited. [Link] Abstract
    Di Wang, Marco Gaboardi and Jinhui Xu.
    Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2018.

  2. Differentially Private Sparse Inverse Covariance Estimation. [Link] Abstract
    Di Wang, Mengdi Huai and Jinhui Xu.
    2018 6th IEEE Global Conference on Signal and Information Processing (2018 GlobalSip).
    Selected as Oral Presentation.

  3. Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning. [Link] Abstract
    Di Wang and Jinhui Xu.
    The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018).
    Selected as Oral Presentation (Acceptance Rate: 411/3800=10.8%).

2017

    Conference Papers

  1. Differentially Private Empirical Risk Minimization Revisited: Faster and More General. [Link] Abstract
    Di Wang, Minwei Ye and Jinhui Xu.
    Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2017.