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Di Wang
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Year
Differentially private empirical risk minimization revisited: Faster and more general
D Wang, M Ye, J Xu
Advances in Neural Information Processing Systems 30, 2017
3172017
Differentially private empirical risk minimization with non-convex loss functions
D Wang, C Chen, J Xu
International Conference on Machine Learning, 6526-6535, 2019
982019
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
772023
Empirical risk minimization in non-interactive local differential privacy revisited
D Wang, M Gaboardi, J Xu
Advances in Neural Information Processing Systems 31, 2018
772018
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
632020
On sparse linear regression in the local differential privacy model
D Wang, J Xu
International Conference on Machine Learning, 6628-6637, 2019
602019
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
512022
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
412019
Inductive graph unlearning
CL Wang, M Huai, D Wang
32nd USENIX Security Symposium (USENIX Security 23), 3205-3222, 2023
372023
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
362021
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
342022
Principal component analysis in the local differential privacy model
D Wang, J Xu
Theoretical computer science 809, 296-312, 2020
342020
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
332020
Noninteractive locally private learning of linear models via polynomial approximations
D Wang, A Smith, J Xu
Algorithmic Learning Theory, 898-903, 2019
33*2019
MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning
S Yang, MA Ali, CL Wang, L Hu, D Wang
arXiv preprint arXiv:2402.11260, 2024
322024
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
282023
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
272023
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
252024
Differentially private natural language models: Recent advances and future directions
L Hu, I Habernal, L Shen, D Wang
arXiv preprint arXiv:2301.09112, 2023
242023
An LLM can Fool Itself: A Prompt-Based Adversarial Attack
X Xu, K Kong, N Liu, L Cui, D Wang, J Zhang, M Kankanhalli
arXiv preprint arXiv:2310.13345, 2023
212023
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