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Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks



Huishuai Zhang , Da Yu , Yiping Lu , Di He

* 13 pages 

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Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent



Da Yu , Gautam Kamath , Janardhan Kulkarni , Tie-Yan Liu , Jian Yin , Huishuai Zhang


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Indiscriminate Poisoning Attacks Are Shortcuts



Da Yu , Huishuai Zhang , Wei Chen , Jian Yin , Tie-Yan Liu


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Differentially Private Fine-tuning of Language Models



Da Yu , Saurabh Naik , Arturs Backurs , Sivakanth Gopi , Huseyin A. Inan , Gautam Kamath , Janardhan Kulkarni , Yin Tat Lee , Andre Manoel , Lukas Wutschitz , Sergey Yekhanin , Huishuai Zhang


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Large Scale Private Learning via Low-rank Reparametrization



Da Yu , Huishuai Zhang , Wei Chen , Jian Yin , Tie-Yan Liu

* Published as a conference paper in International Conference on Machine Learning (ICML 2021). Source code available at https://github.com/dayu11/Differentially-Private-Deep-Learning 

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Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning



Da Yu , Huishuai Zhang , Wei Chen , Tie-Yan Liu

* Published as a conference paper at ICLR 2021. Source code available at https://github.com/dayu11/Gradient-Embedding-Perturbation 

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Membership Inference with Privately Augmented Data Endorses the Benign while Suppresses the Adversary



Da Yu , Huishuai Zhang , Wei Chen , Jian Yin , Tie-Yan Liu


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Gradient Perturbation is Underrated for Differentially Private Convex Optimization



Da Yu , Huishuai Zhang , Wei Chen , Tie-Yan Liu , Jian Yin


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