Следене
Di Wang
Заглавие
Позовавания
Позовавания
Година
Differentially private empirical risk minimization revisited: Faster and more general
D Wang, M Ye, J Xu
Advances in Neural Information Processing Systems 30, 2017
2922017
Differentially private empirical risk minimization with non-convex loss functions
D Wang, C Chen, J Xu
International Conference on Machine Learning, 6526-6535, 2019
872019
Empirical risk minimization in non-interactive local differential privacy revisited
D Wang, M Gaboardi, J Xu
Advances in Neural Information Processing Systems 31, 2018
712018
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
562023
On sparse linear regression in the local differential privacy model
D Wang, J Xu
International Conference on Machine Learning, 6628-6637, 2019
552019
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
512020
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
442022
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
382019
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
342021
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
322020
Principal component analysis in the local differential privacy model
D Wang, J Xu
Theoretical computer science 809, 296-312, 2020
322020
Noninteractive locally private learning of linear models via polynomial approximations
D Wang, A Smith, J Xu
Algorithmic Learning Theory, 898-903, 2019
30*2019
Inductive graph unlearning
CL Wang, M Huai, D Wang
32nd USENIX Security Symposium (USENIX Security 23), 3205-3222, 2023
262023
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
262022
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
232023
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
202023
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
182024
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
182023
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
172020
Practical differentially private and byzantine-resilient federated learning
Z Xiang, T Wang, W Lin, D Wang
Proceedings of the ACM on Management of Data 1 (2), 1-26, 2023
162023
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