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I work on machine learning and privacy at Apple. I received Ph.D. from Princeton University advised by Prof. Prateek Mittal. My dissertation was "Effectively Learning From Data and Generating Data in Differentially Private Machine Learning". I obtained a Bachelor's degree from University of Science and Technology of China (USTC) in 2019. |
xinyut [at] princeton [dot] edu |
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(* indicates equal contribution.) Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang*, Ashwinee Panda*, Milad Nasr, Saeed Mahloujifar, Prateek Mittal Preprint, preliminary version presented at TPDP 2024 (Oral) A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda*, Xinyu Tang*, Saeed Mahloujifar, Vikash Sehwag, Prateek Mittal ICML 2024 Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation Xinyu Tang, Richard Shin, Huseyin A. Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Robert Sim ICLR 2024 Differentially Private Image Classification by Learning Priors from Random Processes Xinyu Tang*, Ashwinee Panda*, Vikash Sehwag, Prateek Mittal NeurIPS 2023 (Spotlight) Effectively Using Public Data in Privacy Preserving Machine Learning Milad Nasr, Saeed Mahloujifar, Xinyu Tang, Prateek Mittal, Amir Houmansadr ICML 2023 Machine Learning with Differentially Private Labels: Mechanisms and Frameworks, Xinyu Tang, Milad Nasr, Saeed Mahloujifar, Virat Shejwalkar, Liwei Song, Amir Houmansadr, Prateek Mittal PETS 2022 Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, Prateek Mittal USENIX Security 2022 |
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Reviewer for JMLR, ICML, NeurIPS, ICLR, AISTATS, AAAI, TPDP |
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Thank Dr. Jon Barron for sharing the source code of his personal page. |