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pro vyhledávání: '"Liang, Zhicong"'
Differentially private (DP) stochastic convex optimization (SCO) is ubiquitous in trustworthy machine learning algorithm design. This paper studies the DP-SCO problem with streaming data sampled from a distribution and arrives sequentially. We also c
Externí odkaz:
http://arxiv.org/abs/2206.08111
Autor:
Wu, Sissi Xiaoxiao, Huang, Zehong, Liang, Zhicong, Gu, Lin, Harada, Tatsuya, Li, Zheng, Zhu, Yingying
Publikováno v:
In Neurocomputing 14 February 2025 618
Publikováno v:
In Journal of Membrane Science February 2025 715
Recently, generalization bounds of the non-convex empirical risk minimization paradigm using Stochastic Gradient Langevin Dynamics (SGLD) have been extensively studied. Several theoretical frameworks have been presented to study this problem from dif
Externí odkaz:
http://arxiv.org/abs/2112.08439
Publikováno v:
In Applied and Computational Harmonic Analysis September 2024 72
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. However, an adversary may still be able to infer the private training data by attacking the released model. Differential pri
Externí odkaz:
http://arxiv.org/abs/2005.00218
Autor:
Chen, Danlin, Wang, Kaifang, Yuan, Ziyi, Lin, Zhihong, Zhang, Manman, Li, Yang, Tang, Jiali, Liang, Zhicong, Li, Ying, Chen, Liu, Li, Longjie, Huang, Xinyi, Pan, Siyu, Zhu, Zhongtai, Hong, Zihao, He, Xuezhong
Publikováno v:
In Carbon Capture Science & Technology June 2023 7
Akademický článek
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Publikováno v:
In Separation and Purification Technology 15 May 2022 289
Publikováno v:
In Carbon Capture Science & Technology March 2022 2