Publications

Please visit the PRADA Lab publication page for the full list of publication of our lab.

( my Master/PhD/Intern/Visiting students)

      2024

      Conference Papers

    1. [IEEE S&P] Preserving Node-level Privacy in Graph Neural Networks [Link] Abstract
      Zihang Xiang, Tianhao Wang, Di Wang
      The 45th IEEE Symposium on Security and Privacy (IEEE S&P 2024).

    2. [VLDB] Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Abstract
      Shaowei Wang, Yun Peng, Jin Li, Zikai Wen, Zhipeng Li, Shiyu Yu, Di Wang, and Wei Yang
      International Conference on Very Large Data Bases (VLDB 2024)

    3. [VLDB] Communication Efficient and Provable Federated Unlearning Abstract
      Youming Tao*, Chenglong Wang*, Miao Pan, Dongxiao Yu, Xiuzhen Cheng, and Di Wang
      International Conference on Very Large Data Bases (VLDB 2024)

    4. [NeurIPS] Revisiting Differentially Private ReLU Regression [Link] Abstract
      Meng Ding, Mingxi Lei, Liyang Zhu, Shaowei Wang, Di Wang, Jinhui Xu.
      The Conference on Neural Information Processing Systems (NeurIPS 2024)

    5. [NeurIPS] Truthful High Dimensional Sparse Linear Regression [Link] Abstract
      Liyang Zhu, Amina Manseur, Meng Ding, Jinyan Liu, Jinhui Xu, Di Wang.
      The Conference on Neural Information Processing Systems (NeurIPS 2024)

    6. [NeurIPS] Perplexity-aware Correction for Robust Alignment with Noisy Preferences [Link] Abstract
      Keyi Kong, Xilie Xu, Di Wang, Jingfeng Zhang, Mohan Kankanhalli.
      The Conference on Neural Information Processing Systems (NeurIPS 2024)

    7. [NeurIPS] Towards Multi-dimensional Explanation Alignment for Medical Classification [Link] Abstract
      Lijie Hu, Songning Lai, Wenshuo Chen, Hongru Xiao, Hongbin Lin, Lu Yu, Jingfeng Zhang, Di Wang.
      The Conference on Neural Information Processing Systems (NeurIPS 2024)

    8. [ICML] Improving Interpretation Faithfulness for Vision Transformers [Link] Abstract
      Lijie Hu*, Yixin Liu*, Ninghao Liu, Mengdi Huai, Lichao Sun, and Di Wang
      The 41st International Conference on Machine Learning (ICML 2024)
      Selected as a spotlight paper

    9. [ICML] Understanding Forgetting in Continual Learning with Linear Regression [Link] Abstract
      Meng Ding, Kaiyi Ji, Di Wang, and Jinhui Xu
      The 41st International Conference on Machine Learning (ICML 2024)

    10. [ICML] Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization [Link] Abstract
      Mudit Gaur, Amrit Bedi, Di Wang, Vaneet Aggarwal
      The 41st International Conference on Machine Learning (ICML 2024)
      Selected as a spotlight paper

    11. [ICLR] Faithful Vision-Language Interpretation via Concept Bottleneck Models [Link] Abstract
      Songning Lai*, Lijie Hu*, Junxiao Wang, Laure Berti-Equille, and Di Wang
      The 12th International Conference on Learning Representations (ICLR 2024)

    12. [ICLR] Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model [Link] Abstract
      Liyang Zhu*, Meng Ding*, Vaneet Aggarwal, Jinhui Xu, and Di Wang
      The 12th International Conference on Learning Representations (ICLR 2024)

    13. [ICLR] Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach [Link] Abstract
      Shaopeng Fu and Di Wang
      The 12th International Conference on Learning Representations (ICLR 2024)

    14. [ICLR] An LLM can Fool Itself: A Prompt-Based Adversarial Attack [Link] Abstract
      Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, and Mohan Kankanhalli
      The 12th International Conference on Learning Representations (ICLR 2024)

    15. [CoLM] Multi-hop Question Answering under Temporal Knowledge Editing [Link] Abstract
      Keyuan Cheng*, Gang Lin*, Haoyang Fei*, Yuxuan Zhai, Lu Yu, Muhammad Asif Ali, Lijie Hu, Di Wang.
      The 1st Conference on Language Modeling (COLM 2024)

    16. [CoLM] Model Autophagy Analysis to Explicate Self-consumption within Human-AI Interactions [Link] Abstract
      Shu Yang*, Muhammad Asif Ali*, Lu Yu, Lijie Hu, and Di Wang
      The 1st Conference on Language Modeling (COLM 2024)

    17. [EMNLP] Dissecting Fine-Tuning Unlearning in Large Language Models [Link] Abstract
      Yihuai Hong, Yuelin Zou, Lijie Hu, Ziqian Zeng, Di Wang, Haiqin Yang.
      The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)

    18. [EMNLP] Private Language Models via Truncated Laplacian Mechanism [Link] Abstract
      Tianhao Huang*, Tao Yang*, Ivan Habernal, Lijie Hu, Di Wang
      The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)

    19. [ACL] Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality [Link] Abstract
      Jiahuan Pei, Haochen Huang, Junxiao Wang, Moonisa Ahsan, Fanghua Ye, Jiang Yiming, Yao Sai, Di Wang, Zhumin Chen, Pengjie Ren, Irene Viola, Pablo Cesar
      The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024 Findings).

    20. [EACL] Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET) [Link] Abstract
      Muhammad Asif Ali, Yan Hu, Jianbin Qin, and Di Wang
      The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024 Findings)

    21. [EACL] Differentially Private Natural Language Models: Recent Advances and Future Directions [Link] Abstract
      Lijie Hu, Ivan Habernal, Lei Shen, and Di Wang
      The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024 Findings)

    22. Journal Papers

    23. [TIT] Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach [Link] Abstract
      Shaopeng Fu, Di Wang
      Revision, IEEE Transactions on Information Theory

    24. [IANDC] Truthful and Privacy-preserving Generalized Linear Models [Link] Abstract
      Yuan Qiu, Jinyan Liu, and Di Wang
      Information and Computation

    25. [JMLR] Faster Rates of Private Stochastic Convex Optimization [Link] Abstract
      Jinyan Su, Lijie Hu, and Di Wang
      Journal of Machine Learning Research

    26. [TMC] Private Over-the-Air Federated Learning at Band-Limited Edge [Link] Abstract
      Youming Tao, Shuzhen Chen, Congwei Zhang, Di Wang, Dongxiao Yu, Xiuzhen Cheng, and Falko Dressler.
      IEEE Transactions on Mobile Computing.

    27. [TKDD] Fair Single Index Model [Link] Abstract
      Yidong Wang*, Meng Ding*, Jinhui Xu and Di Wang
      ACM Transactions on Knowledge Discovery from Data

    28. [TMLR] Persistent Local Homology in Graph Learning [Link] Abstract
      Minghua Wang, Yan Hu, Ziyun Huang, Di Wang, and Jinhui Xu
      Transactions on Machine Learning Research

    29. [TKDE] Towards Stable and Explainable Attention Mechanisms [Link] Abstract
      Lijie Hu*, Xinhai Wang*, Yixin Liu*, Ninghao Liu, Mengdi Huai, Lichao Sun, and Di Wang
      Revision, IEEE Transactions on Knowledge and Data Engineering

    30. [Neural Computation] Generalization Guarantees of Gradient Descent for Shallow Neural Networks [Link] Abstract
      Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou.
      Neural Computation

    31. [TBD] A Multi-classification Division-aggregation Framework for Fake News Detection [Link] Abstract
      Wen Zhang, Haitao Fu, Lionel Z. Wang, Huan Wang, Zhiguo Gong, Pan Zhou, and Di Wang
      IEEE Transactions on Big Data

    32. [TCSS] Multitask Asynchronous Meta-learning for Few-shot Anomalous Node Detection in Dynamic Networks. [Link] Abstract
      Yifan Hong, Lionel Z. WANG, Chuanqi Shi, Junyang Chen, Xiaomei Wei, Huan Wang, Di Wang
      IEEE Transactions on Computational Social Systems

    33. [NL] Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN [Link] Abstract
      Washim Uddin Mondal, Veni Goyal, Goutam Das, Satish V. Ukkusuri, Di Wang, Mohamed-Slim Alouini, and Vaneet Aggarwal
      IEEE Networking Letters

    34. 2023

      Conference Papers

    35. [USENIX] Inductive Graph Unlearning [Link] [Code] Abstract
      Cheng-Long Wang, Mengdi Huai, and Di Wang
      The 32nd USENIX Security Symposium (USENIX 2023)

    36. [IEEE S&P] A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction [Link] [Code] Abstract
      Hanshen Xiao*, Zihang Xiang*, Di Wang, and Srini Devadas (* equal contribution)
      The 44th IEEE Symposium on Security and Privacy (IEEE S&P 2023)

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

    38. [NeurIPS] On Private and Robust Bandits [Link] Abstract
      Yulian Wu*, Xingyu Zhou*, Youming Tao and Di Wang
      2023 Conference on Neural Information Processing Systems (NeurIPS 2023)

    39. [ICML] Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards [Link] Abstract
      Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury and Di Wang
      The 40th International Conference on Machine Learning (ICML 2023)

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

    41. [AAAI] SEAT: Stable and Explainable Attention [Link] 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

    42. [UAI] Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited [Link] Abstract
      Jinyan Su, Changhong Zhao and Di Wang
      The 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023)

    43. [MobiSys] High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile Devices [Link] 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)

    44. [EMNLP] GRI: Graph-based Relative Isomorphism of Word Embedding Spaces [Link] Abstract
      Muhammad Asif Ali, Yan Hu, Jianbin Qin, and Di Wang
      Findings of The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings)

    45. [EMNLP] DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text [Link] Abstract
      Jinyan Su, Terry Yue Zhuo, Di Wang, and Preslav Nakov
      Findings of The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings)

    46. [ECAI] Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm [Link] Abstract
      Di Wang*, Jiahao Ding*, Lijie Hu, Zejun Xie, Miao Pan, and Jinhui Xu
      The 26th European Conference on Artificial Intelligence (ECAI 2023)

    47. [ArabicNLP] GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings. [Link] Abstract
      Muhammad Asif Ali, Maha Alshmrani, Jianbin Qin, Yan Hu, and Di Wang
      The First Arabic Natural Language Processing Conference (ArabicNLP 2023)

      Journal Papers

    48. [JMLR] 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)
      Journal of Machine Learning Research, Volume 24, 132 (2023), Pages 1-57

    49. [TIT] Quantizing Heavy-tailed Data in Statistical Estimation:(Near) Minimax Rates, Covariate Quantization, and Uniform Recovery [Link] Abstract
      Junren Chen, Michael Kwok Po NG, and Di Wang
      IEEE Transactions on Information Theory

    50. [TIT] High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization [Link] Abstract
      Junren Chen, Cheng-Long Wang, Michael Kwok Po NG, and Di Wang
      IEEE Transactions on Information Theory, Volume 69, 8 (2023), Pages 5151-5187

    51. [Science Advances] PPML-Omics: a Privacy-Preserving federated Machine Learning System Protects Patients’ Privacy from Omic Data [Link] Abstract
      Juexiao Zhou*, Siyuan Chen*, Yulian Wu*, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, Wenkai Han, Di Wang, and Xin Gao (* equal contribution)
      Science Advances

    52. [TKDE] Nearly Optimal Rates of Privacy-preserving Sparse Generalized Eigenvalue Problem [Link] Abstract
      Lijie Hu*, Zihang Xiang*, Jiabin Liu, and Di Wang (* equal contribution)
      IEEE Transactions on Knowledge and Data Engineering

    53. [JCSS] PAC Learning Halfspaces in Non-interactive Local Differential Privacy Model with Public Unlabeled Data Abstract
      Jinyan Su, Jinhui Xu, and Di Wang
      Journal of Computer and System Sciences

    54. [CBM] Personalized and Privacy-preserving Federated Heterogeneous Medical Image Analysis with PPPML-HMI [Link] Abstract
      Juexiao Zhou*, Longxi Zhou*, Di Wang, Xiaopeng Xu, Haoyang Li, Yuetan Chu, Wenkai Han, and Xin Gao
      Computers in Biology and Medicine

    55. [TCS] Gradient Complexity and Non-stationary Views of Differentially Private Empirical Risk Minimization Abstract
      Di Wang, and Jinhui Xu
      Theoretical Computer Science

    56. 2022

      Conference Papers

    57. [PODS] 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

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

    59. [ALT] 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)

    60. [AISTATS] 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)

    61. [AISTATS] On Facility Location Problem in Local Differential Privacy Model [Link] 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)

    62. [IJCAI] 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)

    63. [ACML] 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

    64. [ISIT] 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)

    65. 2021

      Conference Papers

    66. [ALT] 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

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

    68. Journal Papers

    69. [TIT] 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

    70. [TCS] 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

    71. [TCS] 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

    72. 2020

      Conference Papers

    73. [ICML] 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)

    74. [AAAI] 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)

    75. [AAAI] 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)

    76. [AAAI] 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)

    77. [ECML-PKDD] 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)

    78. [BIBM] 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%)

    79. Journal Papers

    80. [JMLR] 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

    81. [MLJ] 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)

    82. [TCS] 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

    83. [TCS] 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

    84. [Neurocomputing] 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

    85. 2019

      Conference Papers

    86. [ICML] 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)

    87. [ICML] 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

    88. [NeurIPS] 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

    89. [ALT] 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)

    90. [AAAI] 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%)

    91. [IJCAI] 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)

    92. [IJCAI] 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)

    93. [IJCAI] 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)

    94. [CISS] 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)

    95. Journal Papers

    96. [Neurocomputing] 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

    97. 2018

      Conference Papers

    98. [NeurIPS] 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

    99. [AAAI ] 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%)

    100. [GlobalSip] 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

    101. 2017

      Conference Papers

    102. [NeurIPS] 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
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