| Differentially private empirical risk minimization revisited: Faster and more general D Wang, M Ye, J Xu Advances in Neural Information Processing Systems 30, 2017 | 280 | 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 | 83 | 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 | 67 | 2018 |
| On sparse linear regression in the local differential privacy model D Wang, J Xu International Conference on Machine Learning, 6628-6637, 2019 | 51 | 2019 |
| 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 | 45 | 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 | 37 | 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 | 35 | 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 | 33 | 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 | 30* | 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 | 29 | 2023 |
| 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 | 25 | 2022 |
| High dimensional statistical estimation under uniformly dithered one-bit quantization J Chen, CL Wang, MK Ng, D Wang IEEE Transactions on Information Theory, 2023 | 18 | 2023 |
| Inductive graph unlearning CL Wang, M Huai, D Wang 32nd USENIX Security Symposium (USENIX Security 23), 3205-3222, 2023 | 17 | 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 | 17 | 2020 |
| Differentially Private Pairwise Learning Revisited Z Xue, S Yang, M Huai, D Wang IJCAI 2021, 2021 | 14 | 2021 |
| Privacy-aware Synthesizing for Crowdsourced Data. M Huai, Di Wang 0015, C Miao, J Xu, A Zhang IJCAI, 2542-2548, 2019 | 14 | 2019 |
| 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 | 13 | 2024 |
| 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 | 13 | 2023 |
Jinhui XuProfessor of the State University of New York at Buffalo在 buffalo.edu 的电子邮件经过验证
