| Year | Author | Title | Journal/Conference/Book | Category |
|---|---|---|---|---|
| Oct 28,2023 | Lijie Hu*, Zihang Xiang*, Jiabin Liu, and Di Wang | Nearly Optimal Rates of Privacy-preserving Sparse Generalized Eigenvalue Problem | IEEE Transactions on Knowledge and Data Engineering | Journal Paper |
| Oct 18,2023 | Di Wang and Jinhui Xu | Gradient Complexity and Non-stationary Views of Differentially Private Empirical Risk Minimization | Theoretical Computer Science | Journal Paper |
| Oct 14,2023 | Muhammad Asif Ali, Maha Alshmrani, Jianbin Qin, Yan Hu, Di Wang | GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings | The First Arabic Natural Language Processing Conference (ArabicNLP 2023) | Conference Paper |
| Oct 07,2023 | Muhammad Asif Ali, Yan Hu, Jianbin Qin, and Di Wang | GRI: Graph-based Relative Isomorphism of Word Embedding Spaces | Findings of The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings) | Conference Paper |
| Oct 07,2023 | Jinyan Su, Terry Yue Zhuo, Di Wang, and Preslav Nakov | DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text | Findings of The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings) | Conference Paper |
| Sep 27,2023 | Peiran Dong, Song Guo, Junxiao Wang, Bingjie Wang, Jiewei Zhang, Ziming Liu | Towards Test-Time Refusals via Concept Negation | 2023 Conference on Neural Information Processing Systems (NeurIPS 2023) | Conference Paper |
| Sep 27,2023 | Yulian Wu*, Xingyu Zhou*, Youming Tao and Di Wang | On Private and Robust Bandits | 2023 Conference on Neural Information Processing Systems (NeurIPS 2023) | Conference Paper |
| Aug 14,2023 | Tao Guo, Song Guo, Junxiao Wang, Xueyang Tang, Wenchao Xu | PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models — Federated Learning in Age of Foundation Model | IEEE Transactions on Mobile Computing | Journal Paper |
| Jul 15,2023 | Di Wang*, Jiahao Ding*, Lijie Hu, Zejun Xie, Miao Pan, Jinhui Xu | Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm | The 26th European Conference on Artificial Intelligence (ECAI 2023) | Conference Paper |
| Jun 23,2023 | Wenfei Fan, Resul Tugay, Yaoshu Wang, Min Xie, Muhammad Asif Ali | Learning and Deducing Temporal Orders | Proceedings of the VLDB Endowment (VLDB 2023) | Journal Paper |
| May 28,2023 | Di Wang*, Lijie Hu*, Huanyu Zhang, Marco Gaboardi, Jinhui Xu | Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data | Journal of Machine Learning Research | Journal Paper |
| May 18,2023 | Peiran Dong, Song Guo, Junxiao Wang | Investigating Trojan Attacks on Pre-trained Language Model-powered Database Middleware | ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023) | Conference Paper |
| May 08,2023 | Jinyan Su, Changhong Zhao, Di Wang | Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited | The 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023) | Conference Paper |
| Apr 26,2023 | Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang | Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards | The 40th International Conference on Machine Learning (ICML 2023) | Conference Paper |
| Apr 23,2023 | Junren Chen, Cheng-Long Wang, Michael Kwok Po NG, Di Wang | High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization | IEEE Transactions on Information Theory | Journal Paper |
| Apr 23,2023 | Cheng-Long Wang, Mengdi Huai, Di Wang | Inductive Graph Unlearning | The 32nd USENIX Security Symposium (USENIX 2023) | Conference Paper |
| Apr 23,2023 | Hanshen Xiao*, Zihang Xiang*, Di Wang, Srini Devadas | A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction | The 44th IEEE Symposium on Security and Privacy (IEEE S&P 2023) | Conference Paper |
| Apr 22,2023 | Lijie Hu*, Zihang Xiang*, Jiabin Liu, Di Wang | Privacy-preserving Sparse Generalized Eigenvalue Problem | The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023) | Conference Paper |
| Mar 14,2023 | Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, Song Guo | PMR: Prototypical Modal Rebalance for Multimodal Learning | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023) | Conference Paper |
| Mar 14,2023 | Zihang Xiang, Tianhao Wang , Wanyu Lin, Di Wang | On Practical Differentially Private and Byzantine-resilient Federated Learning | International Conference on Management of Data (SIGMOD 2023) | Conference Paper |
| Mar 14,2023 | Rui Chen, Qiyu Wan, Xinyue Zhang, Xiaoqi Qin, Di Wang, Xin Fu, Miao Pan | High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile Devices | The 21st ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2023) | Conference Paper |
| Feb 07,2023 | Lijie Hu*, Yixin Liu *, Ninghao Liu , Mengdi Huai, Lichao Sun, Di Wang | SEAT: Stable and Explainable Attention | The 37th AAAI Conference on Artificial Intelligence (AAAI 2023) | Conference Paper |
| Dec 15,2022 | Jinyan Su, Jinhui Xu, Di Wang | On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data | The 14th Asian Conference on Machine Learning (ACML 2022) | Conference Paper |
| Dec 10,2022 | Yuan Qiu, Jinyan Liu, Di Wang | Truthful Generalized Linear Models | The 18th Conference on Web and Internet Economics (WINE 2022) | Conference Paper |
| Aug 01,2022 | Youming Tao, Yulian Wu, Xiuzhen Cheng, Di Wang | Private Stochastic Convex Optimization and Sparse Learning with Heavy-tailed Data Revisited | The 31st International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2022) | Conference Paper |
| Jun 15,2022 | Di Wang, Jinhui Xu | Differentially Private $ell_1$-norm Linear Regression with Heavy-tailed Data | 2022 IEEE International Symposium on Information Theory (ISIT 2022) | Conference Paper |
| Jun 15,2022 | Lijie Hu, Shuo Ni, Hanshen Xiao, Di Wang | High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data | The 41st ACM Symposium on Principles of Database Systems (PODS 2022) | Conference Paper |
| Apr 20,2022 | Jinyan Su, Lijie Hu, Di Wang | Faster Rates of Private Stochastic Convex Optimization | The 33rd International Conference on Algorithmic Learning Theory (ALT 2022) | Conference Paper |
| Apr 15,2022 | Youming Tao*, Yulian Wu*, Peng Zhao, Di Wang | Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits | The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022) | Conference Paper |
| Apr 15,2022 | Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboradi, Shi Li, Di Wang | On Facility Location Problem in Local Differential Privacy Model | The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022) | Conference Paper |
| Aug 15,2021 | Zhiyu Xue*, Shaoyang Yang*, Mengdi Huai, Di Wang | Differentially Private Pairwise Learning Revisited | The 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) | Conference Paper |
| Apr 15,2021 | Di Wang*, Huanyu Zhang*, Marco Gaboardi, Jinhui Xu | Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data | The 32nd International Conference on Algorithmic Learning Theory (ALT 2021) | Conference Paper |
| Apr 14,2021 | Di Wang, Jinhui Xu | Differentially Private High Dimensional Sparse Covariance Matrix Estimation | Theoretical Computer Science | Journal Paper |
| Apr 01,2021 | Di Wang, Jinhui Xu | Inferring Ground Truth From Crowdsourced Data Under Local Attribute Differential Privacy | Theoretical Computer Science | Journal Paper |
| Feb 01,2021 | Di Wang, Jinhui Xu | On Sparse Linear Regression in the Local Differential Privacy Model | IEEE Transactions on Information Theory | Journal Paper |
| Dec 01,2020 | Mengdi Huai, Chenglin Miao, Jinduo Liu, Di Wang, Jingyuan Chou, Aidong Zhang | Global Interpretation for Patient Similarity Learning | The 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020) | Conference Paper |
| Nov 01,2020 | Di Wang*, Xiangyu Guo*, Shi Li, Jinhui Xu | Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding | Machine Learning Journal | Journal Paper |
| Sep 15,2020 | Di Wang, Jinhui Xu | Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method | The 2020 European Conference on Machine Learning (ECML-PKDD 2020) | Conference Paper |
| Sep 01,2020 | Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu | Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy | Journal of Machine Learning Research | Journal Paper |
| Jul 15,2020 | Di Wang*, Hanshen Xiao*, Srini Devadas, Jinhui Xu | On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data | The 37th International Conference on Machine Learning (ICML 2020) | Conference Paper |
| Jul 01,2020 | Di Wang*, Xiangyu Guo*, Chaowen Guan, Shi Li, Jinhui Xu | Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably | Neurocomputing | Journal Paper |
| May 01,2020 | Di Wang, Jinhui Xu | Tight Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation | Theoretical Computer Science | Journal Paper |
| Feb 01,2020 | Di Wang, Jinhui Xu | Principal Component Analysis in the Local Differential Privacy Model | Theoretical Computer Science | Journal Paper |
| Feb 01,2020 | Di Wang*, Xiangyu Guo*, Chaowen Guan, Shi Li, Jinhui Xu | Scalable Estimating Stochastic Linear Combination of Non-linear Regressions | The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020) | Conference Paper |
| Feb 01,2020 | Mengdi Huai*, Di Wang*, Chenglin Miao, Jinhui Xu, Aidong Zhang | Pairwise Learning with Differential Privacy Guarantees | Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020) | Conference Paper |
| Feb 01,2020 | Mengdi Huai, Di Wang, Chenglin Miao, Aidong Zhang | Towards Interpretation of Pairwise Learning | The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020) | Conference Paper |
| Dec 01,2019 | Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang | Facility Location Problem in Differential Privacy Model Revisited | Conference on Neural Information Processing Systems (NeurIPS 2019) | Conference Paper |
| Oct 01,2019 | Di Wang, Jinhui Xu | Faster Large Scale Constrained Linear Regression via Two-Step Preconditioning | Neurocomputing | Journal Paper |
| Aug 15,2019 | Di Wang, Jinhui Xu | Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation | The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019) | Conference Paper |
| Aug 15,2019 | Di Wang, Jinhui Xu | Principal Component Analysis in the Local Differential Privacy Model | The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019) | Conference Paper |
| Aug 15,2019 | Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang | Privacy-aware Synthesizing for Crowdsourced Data | The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019) | Conference Paper |
| Jul 15,2019 | Di Wang, Changyou Chen, Jinhui Xu | Differentially Private Empirical Risk Minimization with Non-convex Loss Functions | The 36th International Conference on Machine Learning (ICML 2019) | Conference Paper |
| Jul 15,2019 | Di Wang, Jinhui Xu | On Sparse Linear Regression in the Local Differential Privacy Model | The 36th International Conference on Machine Learning (ICML 2019) | Conference Paper |
| Apr 15,2019 | Di Wang, Jinhui Xu, Yang He | Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding | The 53rd Annual Conference on Information Sciences and Systems (CISS 2019) | Conference Paper |
| Apr 01,2019 | Di Wang, Adam Smith, Jinhui Xu | Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations | The 30th International Conference on Algorithmic Learning Theory (ALT 2019) | Conference Paper |
| Feb 01,2019 | Di Wang, Jinhui Xu | Differentially Private Empirical Risk Minimization with Smooth Non-convex Loss Functions: A Non-stationary View | The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019) | Conference Paper |
| Dec 01,2018 | Di Wang, Marco Gaboardi, Jinhui Xu | Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited | Conference on Neural Information Processing Systems (NeurIPS 2018) | Conference Paper |
| Apr 01,2018 | Di Wang, Mengdi Huai, Jinhui Xu | Differentially Private Sparse Inverse Covariance Estimation | 2018 6th IEEE Global Conference on Signal and Information Processing (2018 GlobalSip) | Conference Paper |
| Feb 01,2018 | Di Wang, Jinhui Xu | Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning | The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018) | Conference Paper |
| Dec 01,2017 | Di Wang, Minwei Ye, Jinhui Xu | Differentially Private Empirical Risk Minimization Revisited: Faster and More General | Conference on Neural Information Processing Systems (NeurIPS 2017) | Conference Paper |
