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ORCIDGalen Andrew
- > Home > Persons > Galen Andrew
Publications
- 2025
[i9]Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Cristóbal Guzmán, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis:
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy. CoRR abs/2512.03238 (2025)- 2024
- [c16]Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh Brendan McMahan, Vinith Menon Suriyakumar:
One-shot Empirical Privacy Estimation for Federated Learning. ICLR 2024 - 2023
- [c15]Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang:
Federated Learning of Gboard Language Models with Differential Privacy. ACL (industry) 2023: 629-639 - [c14]Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh:
Unleashing the Power of Randomization in Auditing Differentially Private ML. NeurIPS 2023 - [i8]Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith M. Suriyakumar:
One-shot Empirical Privacy Estimation for Federated Learning. CoRR abs/2302.03098 (2023) - [i7]Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh:
Unleashing the Power of Randomization in Auditing Differentially Private ML. CoRR abs/2305.18447 (2023) - [i6]Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang:
Federated Learning of Gboard Language Models with Differential Privacy. CoRR abs/2305.18465 (2023) - 2021
- [c13]Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy:
Differentially Private Learning with Adaptive Clipping. NeurIPS 2021: 17455-17466 - [i5]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth
, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - 2020
- [i4]Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Françoise Beaufays:
Training Production Language Models without Memorizing User Data. CoRR abs/2009.10031 (2020) - 2019
- [i3]Om Thakkar, Galen Andrew, H. Brendan McMahan:
Differentially Private Learning with Adaptive Clipping. CoRR abs/1905.03871 (2019) - 2018
- [i1]H. Brendan McMahan, Galen Andrew:
A General Approach to Adding Differential Privacy to Iterative Training Procedures. CoRR abs/1812.06210 (2018)
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