Di Wang

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Jinhui XuProfessor of the State University of New York at BuffaloVerified email at buffalo.edu
Lijie HuKAUSTVerified email at kaust.edu.sa
Mengdi HuaiIowa State UniversityVerified email at iastate.edu
Marco GaboardiBoston UniversityVerified email at bu.edu
Yulian WuKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Hanshen XiaoPurdue University / NVIDIAVerified email at mit.edu
Chenglin MiaoAssistant Professor of Computer Science, Iowa State UniversityVerified email at iastate.edu
Adam SmithProfessor of Computer Science, Boston UniversityVerified email at bu.edu
Aidong Zhang, Fellow of ACM, IEEE, a...Thomas M. Linville Professor of Computer Science, University of VirginiaVerified email at virginia.edu
Youming TaoTechnische Universität Berlin & Shandong UniversityVerified email at tkn.tu-berlin.de
Huanyu ZhangMetaVerified email at cornell.edu
Miao PanAssociate Professor of Electrical and Computer Engineering, University of HoustonVerified email at uh.edu
Srinivas DevadasEdwin Sibley Webster Professor of Electrical Engineering and Computer Science, MITVerified email at mit.edu
Shi LiProfessor, Nanjing UniversityVerified email at nju.edu.cn
Chen ChangyouAssistant Professor at University at BuffaloVerified email at buffalo.edu
Michael Kwok-Po NgSIAM Fellow, AAIA Fellow, Dean of Science, Chair Professor in Mathematics & in Data Science, HKBUVerified email at hkbu.edu.hk
Jiahao DingApplied Scientist, AmazonVerified email at amazon.com
Peng ZhaoNanjing UniversityVerified email at lamda.nju.edu.cn
Xiangyu GuoFacebook, University at BuffaloVerified email at buffalo.edu
Baochun LiProfessor of Electrical and Computer Engineering, University of TorontoVerified email at ece.toronto.edu

Di Wang

King Abdullah University of Science and Technology

Verified email at kaust.edu.sa - Homepage

Differential Privacy Machine Unlearning Knowledge Editing


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Differentially private empirical risk minimization revisited: Faster and more general D Wang, M Ye, J Xu Advances in Neural Information Processing Systems 30, 2017	286	2017
Differentially private empirical risk minimization with non-convex loss functions D Wang, C Chen, J Xu International Conference on Machine Learning, 6526-6535, 2019	85	2019
Empirical risk minimization in non-interactive local differential privacy revisited D Wang, M Gaboardi, J Xu Advances in Neural Information Processing Systems 31, 2018	68	2018
On sparse linear regression in the local differential privacy model D Wang, J Xu International Conference on Machine Learning, 6628-6637, 2019	55	2019
Detectllm: Leveraging log rank information for zero-shot detection of machine-generated text J Su, TY Zhuo, D Wang, P Nakov arXiv preprint arXiv:2306.05540, 2023	52	2023
On differentially private stochastic convex optimization with heavy-tailed data D Wang, H Xiao, S Devadas, J Xu International Conference on Machine Learning, 10081-10091, 2020	50	2020
High dimensional differentially private stochastic optimization with heavy-tailed data L Hu, S Ni, H Xiao, D Wang Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2022	42	2022
Differentially private empirical risk minimization with smooth non-convex loss functions: A non-stationary view D Wang, J Xu Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1182-1189, 2019	38	2019
Estimating smooth glm in non-interactive local differential privacy model with public unlabeled data D Wang, H Zhang, M Gaboardi, J Xu Algorithmic Learning Theory, 1207-1213, 2021	33	2021
Principal component analysis in the local differential privacy model D Wang, J Xu Theoretical computer science 809, 296-312, 2020	32	2020
Pairwise learning with differential privacy guarantees M Huai, D Wang, C Miao, J Xu, A Zhang Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 694-701, 2020	31	2020
Noninteractive locally private learning of linear models via polynomial approximations D Wang, A Smith, J Xu Algorithmic Learning Theory, 898-903, 2019	29*	2019
Optimal rates of (locally) differentially private heavy-tailed multi-armed bandits Y Tao, Y Wu, P Zhao, D Wang International Conference on Artificial Intelligence and Statistics, 1546-1574, 2022	26	2022
Fake news detectors are biased against texts generated by large language models J Su, TY Zhuo, J Mansurov, D Wang, P Nakov arXiv preprint arXiv:2309.08674, 2023	22	2023
Inductive graph unlearning CL Wang, M Huai, D Wang 32nd USENIX Security Symposium (USENIX Security 23), 3205-3222, 2023	22	2023
Faithful vision-language interpretation via concept bottleneck models S Lai, L Hu, J Wang, L Berti-Equille, D Wang The Twelfth International Conference on Learning Representations, 2023	19	2023
High dimensional statistical estimation under uniformly dithered one-bit quantization J Chen, CL Wang, MK Ng, D Wang IEEE Transactions on Information Theory 69 (8), 5151-5187, 2023	18	2023
Differentially private natural language models: Recent advances and future directions L Hu, I Habernal, L Shen, D Wang arXiv preprint arXiv:2301.09112, 2023	16	2023
Empirical risk minimization in the non-interactive local model of differential privacy D Wang, M Gaboardi, A Smith, J Xu Journal of machine learning research 21 (200), 1-39, 2020	16	2020
PPML-Omics: a privacy-preserving federated machine learning method protects patients’ privacy in omic data J Zhou, S Chen, Y Wu, H Li, B Zhang, L Zhou, Y Hu, Z Xiang, Z Li, N Chen, ... Science Advances 10 (5), eadh8601, 2024	15	2024

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