Di Wang's Publications
   


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

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).
  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%) .

  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.

Workshop Papers

  1. 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)
    NeurIPS 2019 Workshop on Privacy in Machine Learning.

  2. High Dimensional Sparse Linear Regression under Local Differential Privacy: Power and Limitations. [Link] Abstract
    Di Wang, Adam Smith and Jinhui Xu.
    NeurIPS 2018 Workshop on Privacy Preserving Machine Learning.