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

**[***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).**[***VLDB*]*Privacy Amplification via Shuffling: Unified, Simplified, and Tightened*Abstract▼

Shaowei Wang, Yun Peng, Jin Li, Zikai Wen, Zhipeng Li, Shiyu Yu,, and Wei Yang**Di Wang**

International Conference on Very Large Data Bases (VLDB 2024)**[***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)**[***NeurIPS*]**Revisiting Differentially Private ReLU Regression****[Link] Abstract▼**

Meng Ding, Mingxi Lei, Liyang Zhu, Shaowei Wang,, Jinhui Xu.**Di Wang**

The Conference on Neural Information Processing Systems (NeurIPS 2024)

**[***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)

**[***NeurIPS*]**Perplexity-aware Correction for Robust Alignment with Noisy Preferences****[Link] Abstract▼**

Keyi Kong, Xilie Xu,, Jingfeng Zhang, Mohan Kankanhalli.**Di Wang**

The Conference on Neural Information Processing Systems (NeurIPS 2024)

**[***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)

**[***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**

**[***ICML*]**Understanding Forgetting in Continual Learning with Linear Regression****[Link] Abstract▼**

Meng Ding, Kaiyi Ji,, and Jinhui Xu**Di Wang**

The 41st International Conference on Machine Learning (ICML 2024)

**[***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,, Vaneet Aggarwal**Di Wang**

The 41st International Conference on Machine Learning (ICML 2024)

**Selected as a spotlight paper**

**[***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)**[***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)**[***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)**[***ICLR*]**An LLM can Fool Itself: A Prompt-Based Adversarial Attack****[Link] Abstract▼**

Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui,, Jingfeng Zhang, and Mohan Kankanhalli**Di Wang**

The 12th International Conference on Learning Representations (ICLR 2024)**[***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)**[***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)**[***EMNLP*]**Dissecting Fine-Tuning Unlearning in Large Language Models****[Link] Abstract▼**

Yihuai Hong, Yuelin Zou, Lijie Hu^{★}, Ziqian Zeng,, Haiqin Yang.**Di Wang**

The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)**[***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)**[***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,, Zhumin Chen, Pengjie Ren, Irene Viola, Pablo Cesar**Di Wang**

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024 Findings).**[***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)**[***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)**[***TIT*]*Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach*[Link] Abstract▼

Shaopeng Fu^{★},**Di Wang**

Revision, IEEE Transactions on Information Theory**[***IANDC*]*Truthful and Privacy-preserving Generalized Linear Models*[Link] Abstract▼

Yuan Qiu^{★}, Jinyan Liu, and**Di Wang**

Information and Computation**[***JMLR*]*Faster Rates of Private Stochastic Convex Optimization*[Link] Abstract▼

Jinyan Su^{★}, Lijie Hu^{★}, and**Di Wang**

Journal of Machine Learning Research**[***TMC*]**Private Over-the-Air Federated Learning at Band-Limited Edge****[Link] Abstract▼**

Youming Tao, Shuzhen Chen, Congwei Zhang,, Dongxiao Yu, Xiuzhen Cheng, and Falko Dressler.**Di Wang**

IEEE Transactions on Mobile Computing.**[***TKDD*]**Fair Single Index Model****[Link] Abstract▼**

Yidong Wang^{★*}, Meng Ding^{★*}, Jinhui Xu and**Di Wang**

ACM Transactions on Knowledge Discovery from Data**[***TMLR*]**Persistent Local Homology in Graph Learning****[Link] Abstract▼**

Minghua Wang, Yan Hu, Ziyun Huang,, and Jinhui Xu**Di Wang**

Transactions on Machine Learning Research**[***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**[***Neural Computation*]**Generalization Guarantees of Gradient Descent for Shallow Neural Networks****[Link] Abstract▼**

Puyu Wang, Yunwen Lei,, Yiming Ying, Ding-Xuan Zhou.**Di Wang**

Neural Computation**[***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**[***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**[***NL*]**Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN****[Link] Abstract▼**

Washim Uddin Mondal, Veni Goyal, Goutam Das, Satish V. Ukkusuri,, Mohamed-Slim Alouini, and Vaneet Aggarwal**Di Wang**

IEEE Networking Letters**[***USENIX*]**Inductive Graph Unlearning****[Link] [Code] Abstract▼**

Cheng-Long Wang^{★}, Mengdi Huai, and**Di Wang**

The 32nd USENIX Security Symposium (USENIX 2023)**[***IEEE S&P*]**A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction****[Link] [Code] Abstract▼**

Hanshen Xiao^{*}, Zihang Xiang^{*★},, and Srini Devadas (* equal contribution)**Di Wang**

The 44th IEEE Symposium on Security and Privacy (IEEE S&P 2023)**[***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)**[***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)**[***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)**[***AISTATS*]**Privacy-preserving Sparse Generalized Eigenvalue Problem****[Link] Abstract▼**

Lijie Hu^{*★}, Zihang Xiang^{*★}, Jiabin Liu, and(* equal contribution)**Di Wang**

The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)**[***AAAI*]*SEAT: Stable and Explainable Attention*[Link] Abstract▼

Lijie Hu^{*★}, Yixin Liu^{*}, Ninghao Liu , Mengdi Huai, Lichao Sun, and(* equal contribution)**Di Wang**

The 37th AAAI Conference on Artificial Intelligence (AAAI 2023)

**Selected as an Oral paper**

**[***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)

**[***MobiSys*]**High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile Devices****[Link] Abstract▼**

Rui Chen, Qiyu Wan, Xinyue Zhang, Xiaoqi Qin,, Xin Fu, and Miao Pan**Di Wang**

The 21st ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2023)

**[***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)

**[***EMNLP*]**DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text****[Link] Abstract▼**

Jinyan Su, Terry Yue Zhuo,, and Preslav Nakov**Di Wang**

Findings of The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings)

**[***ECAI*]**Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm****[Link] Abstract▼**

, Jiahao Ding**Di Wang**^{*}^{*}, Lijie Hu, Zejun Xie, Miao Pan, and Jinhui Xu

The 26th European Conference on Artificial Intelligence (ECAI 2023)

**[***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***[***JMLR*]*Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data*[Link] Abstract▼

, Lijie Hu**Di Wang**^{*}^{*}^{★}, Huanyu Zhang, Marco Gaboardi, and Jinhui Xu (* equal contribution)

Journal of Machine Learning Research, Volume 24, 132 (2023), Pages 1-57**[***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**[***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**[***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,, and Xin Gao (* equal contribution)**Di Wang**

Science Advances**[***TKDE*]*Nearly Optimal Rates of Privacy-preserving Sparse Generalized Eigenvalue Problem*[Link] Abstract▼

Lijie Hu^{*★}, Zihang Xiang^{*★}, Jiabin Liu, and(* equal contribution)**Di Wang**

IEEE Transactions on Knowledge and Data Engineering**[***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**[***CBM*]*Personalized and Privacy-preserving Federated Heterogeneous Medical Image Analysis with PPPML-HMI*[Link] Abstract▼

Juexiao Zhou^{*}, Longxi Zhou^{*},, Xiaopeng Xu, Haoyang Li, Yuetan Chu, Wenkai Han, and Xin Gao**Di Wang**

Computers in Biology and Medicine**[***TCS*]*Gradient Complexity and Non-stationary Views of Differentially Private Empirical Risk Minimization*Abstract▼

, and Jinhui Xu**Di Wang**

Theoretical Computer Science**[***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**[***WINE*]*Truthful Generalized Linear Models*[Link] Abstract▼

Yuan Qiu^{★}, Jinyan Liu, and**Di Wang**

The 18th Conference on Web and Internet Economics (WINE 2022)**[***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)**[***AISTATS*]*Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits*[Link] Abstract▼

Youming Tao^{*★}, Yulian Wu^{*★}, Peng Zhao, and(* equal contribution)**Di Wang**

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)

**[***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)**[***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)**[***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****[***ISIT*]*Differentially Private $\ell_1$-norm Linear Regression with Heavy-tailed Data*[Link] Abstract▼

and Jinhui Xu**Di Wang**

2022 IEEE International Symposium on Information Theory (ISIT 2022)**[***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**[***IJCAI*]*Differentially Private Pairwise Learning Revisited*[Link] Abstract▼

Zhiyu Xue^{*}^{★}, Shaoyang Yang^{*}^{★}, Mengdi Huai and(* equal contribution)**Di Wang**

The 30th International Joint Conference on Artificial Intelligence (IJCAI 2021)**[***TIT*]*On Sparse Linear Regression in the Local Differential Privacy Model*[Link] Abstract▼

and Jinhui Xu**Di Wang**

IEEE Transactions on Information Theory, Volume 67, no. 2, Pages 1182-1200, Feb. 2021**[***TCS*]*Inferring Ground Truth From Crowdsourced Data Under Local Attribute Differential Privacy*[Link] Abstract▼

and Jinhui Xu**Di Wang**

Theoretical Computer Science Volume 865, 14 April 2021, Pages 85-98**[***TCS*]*Differentially Private High Dimensional Sparse Covariance Matrix Estimation*[Link] Abstract▼

and Jinhui Xu**Di Wang**

Theoretical Computer Science Volume 865, 14 April 2021, Pages 119-130**[***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)**[***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)**[***AAAI*]*Pairwise Learning with Differential Privacy Guarantees*[Link] Abstract▼

**Mengdi Huai**,^{*}, Chenglin Miao, Jinhui Xu and Aidong Zhang (* equal contribution)**Di Wang**^{*}

Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)**[***AAAI*]*Towards Interpretation of Pairwise Learning*[Link] Abstract▼

Mengdi Huai,, Chenglin Miao and Aidong Zhang**Di Wang**

The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)**[***ECML-PKDD*]*Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method*[Link] Abstract▼

and Jinhui Xu**Di Wang**

The 2020 European Conference on Machine Learning (ECML-PKDD 2020)**[***BIBM*]*Global Interpretation for Patient Similarity Learning*[Link] Abstract▼

Mengdi Huai, Chenglin Miao, Jinduo Liu,, Jingyuan Chou and Aidong Zhang.**Di Wang**

The 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020)

**Selected as Regular Paper (Acceptance Rate: 19.4%)****[***JMLR*]*Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy*[Link] Abstract▼

, Marco Gaboardi, Adam Smith and Jinhui Xu**Di Wang**

Journal of Machine Learning Research, Volume 21, 200 (2020), Pages 1-39**[***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)

**[***TCS*]*Tight Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation*[Link] Abstract▼

and Jinhui Xu**Di Wang**

Theoretical Computer Science, Volume 815, 2 May 2020, Pages 47-59**[***TCS*]*Principal Component Analysis in the Local Differential Privacy Model*[Link] Abstract▼

and Jinhui Xu**Di Wang**

Theoretical Computer Science, Volume 809, 24 February 2020, Pages 296-312

**[***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**[***ICML*]*Differentially Private Empirical Risk Minimization with Non-convex Loss Functions*[Link] Abstract▼

, Changyou Chen and Jinhui Xu**Di Wang**

The 36th International Conference on Machine Learning (ICML 2019)**[***ICML*]*On Sparse Linear Regression in the Local Differential Privacy Model*[Link] Abstract▼

and Jinhui Xu**Di Wang**

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

**[***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**[***ALT*]*Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations*[Link] Abstract▼

, Adam Smith and Jinhui Xu**Di Wang**

The 30th International Conference on Algorithmic Learning Theory (ALT 2019)**[***AAAI*]*Differentially Private Empirical Risk Minimization with Smooth Non-convex Loss Functions: A Non-stationary View*[Link] Abstract▼

and Jinhui Xu**Di Wang**

The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019)

**Selected as Oral Presentation (Acceptance Rate: 460/7095=6.5%)****[***IJCAI*]*Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation*[Link] Abstract▼

and Jinhui Xu**Di Wang**

The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)**[***IJCAI*]*Principal Component Analysis in the Local Differential Privacy Model*[Link] Abstract▼

and Jinhui Xu**Di Wang**

The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)**[***IJCAI*]*Privacy-aware Synthesizing for Crowdsourced Data*[Link] Abstract▼

Mengdi Huai,, Chenglin Miao, Jinhui Xu, Aidong Zhang**Di Wang**

The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)**[***CISS*]*Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding*[Link] Abstract▼

, Jinhui Xu and Yang He**Di Wang**

The 53rd Annual Conference on Information Sciences and Systems (CISS 2019)**[***Neurocomputing*]*Faster Large Scale Constrained Linear Regression via Two-Step Preconditioning*[Link] Abstract▼

and Jinhui Xu**Di Wang**

Neurocomputing, Volume 364, 28 October 2019, Pages 280-296**[***NeurIPS*]*Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited*[Link] Abstract▼

, Marco Gaboardi and Jinhui Xu**Di Wang**

Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2018**[***AAAI*]*Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning*[Link] Abstract▼

and Jinhui Xu**Di Wang**

The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018)

**Selected as Oral Presentation (Acceptance Rate: 411/3800=10.8%)****[***GlobalSip*]*Differentially Private Sparse Inverse Covariance Estimation*[Link] Abstract▼

, Mengdi Huai and Jinhui Xu**Di Wang**

2018 6th IEEE Global Conference on Signal and Information Processing (2018 GlobalSip)**Selected as Oral Presentation****[***NeurIPS*]*Differentially Private Empirical Risk Minimization Revisited: Faster and More General*[Link] Abstract▼

, Minwei Ye and Jinhui Xu**Di Wang**

Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2017