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ORCIDZachary Charles
Person information
- affiliation: Google Research
- affiliation (PhD 2017): University of Wisconsin-Madison, Department of Electrical and Computer Engineering, Madison, WI, USA
- affiliation: University of Pennsylvania, Department of Mathematics, Philadelphia, PA, USA
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2020 – today
- 2024
[j6]Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite:
Leveraging Function Space Aggregation for Federated Learning at Scale. Trans. Mach. Learn. Res. 2024 (2024)- [i29]Keith Rush, Zachary Charles, Zachary Garrett:
FAX: Scalable and Differentiable Federated Primitives in JAX. CoRR abs/2403.07128 (2024) - [i28]Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush:
Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR abs/2407.07737 (2024) - 2023
- [c18]Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný:
A Rate-Distortion View on Model Updates. Tiny Papers @ ICLR 2023 - [c17]Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett:
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. NeurIPS 2023 - [c16]Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan:
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS 2023 - [i27]Keith Rush, Zachary Charles, Zachary Garrett:
Federated Automatic Differentiation. CoRR abs/2301.07806 (2023) - [i26]Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan:
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR abs/2302.01463 (2023) - [i25]Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett:
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. CoRR abs/2307.09619 (2023) - [i24]Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite:
Leveraging Function Space Aggregation for Federated Learning at Scale. CoRR abs/2311.10291 (2023) - 2022
- [c15]Zachary Charles, Keith Rush:
Iterated Vector Fields and Conservatism, with Applications to Federated Learning. ALT 2022: 130-147 - [c14]Gary Cheng, Zachary Charles, Zachary Garrett, Keith Rush:
Does Federated Dropout actually work? CVPR Workshops 2022: 3386-3394 - [i23]Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný:
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory. CoRR abs/2201.02664 (2022) - [i22]Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith:
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR abs/2206.09262 (2022) - [i21]Zachary Charles, Kallista A. Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Agüera y Arcas:
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning. CoRR abs/2208.09432 (2022) - 2021
- [j5]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis
, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [c13]Zachary Charles, Jakub Konecný:
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. AISTATS 2021: 2575-2583 - [c12]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan:
Adaptive Federated Optimization. ICLR 2021 - [c11]Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith:
On Large-Cohort Training for Federated Learning. NeurIPS 2021: 20461-20475 - [i20]Zachary Charles, Jakub Konecný:
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. CoRR abs/2103.05032 (2021) - [i19]Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, Gauri Joshi:
Local Adaptivity in Federated Learning: Convergence and Consistency. CoRR abs/2106.02305 (2021) - [i18]Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith:
On Large-Cohort Training for Federated Learning. CoRR abs/2106.07820 (2021) - [i17]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) - [i16]Zachary Charles, Keith Rush:
Iterated Vector Fields and Conservatism, with Applications to Federated Learning. CoRR abs/2109.03973 (2021) - 2020
- [i15]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan:
Adaptive Federated Optimization. CoRR abs/2003.00295 (2020) - [i14]Zachary Charles, Jakub Konecný:
On the Outsized Importance of Learning Rates in Local Update Methods. CoRR abs/2007.00878 (2020)
2010 – 2019
- 2019
- [c10]Zachary Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos:
A Geometric Perspective on the Transferability of Adversarial Directions. AISTATS 2019: 1960-1968 - [c9]Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? ICML 2019: 5321-5330 - [c8]Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. NeurIPS 2019: 10320-10330 - [i13]Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding. CoRR abs/1901.09671 (2019) - [i12]Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? CoRR abs/1905.03177 (2019) - [i11]Zachary Charles, Shashank Rajput, Stephen J. Wright, Dimitris S. Papailiopoulos:
Convergence and Margin of Adversarial Training on Separable Data. CoRR abs/1905.09209 (2019) - [i10]Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. CoRR abs/1907.12205 (2019) - [i9]Scott Sievert, Zachary Charles:
Improving the convergence of SGD through adaptive batch sizes. CoRR abs/1910.08222 (2019) - [i8]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [j4]Zachary Charles, Nigel Boston:
Exploiting algebraic structure in global optimization and the Belgian chocolate problem. J. Glob. Optim. 72(2): 241-254 (2018) - [j3]Zachary Charles:
Generating random factored ideals in number fields. Math. Comput. 87(312): 2047-2056 (2018) - [c7]Zachary Charles, Amin Jalali, Rebecca Willett:
Sparse Subspace Clustering with Missing and Corrupted Data. DSW 2018: 180-184 - [c6]Zachary Charles, Dimitris S. Papailiopoulos:
Stability and Generalization of Learning Algorithms that Converge to Global Optima. ICML 2018: 744-753 - [c5]Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients. ICML 2018: 902-911 - [c4]Alisha Zachariah, Zachary Charles, Nigel Boston, Bernard C. Lesieutre:
Distributions of the Number of Solutions to the Network Power Flow Equations. ISCAS 2018: 1-5 - [c3]Zachary Charles, Dimitris S. Papailiopoulos:
Gradient Coding Using the Stochastic Block Model. ISIT 2018: 1998-2002 - [c2]Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. NeurIPS 2018: 9872-9883 - [i7]Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DRACO: Robust Distributed Training via Redundant Gradients. CoRR abs/1803.09877 (2018) - [i6]Zachary Charles, Dimitris S. Papailiopoulos:
Gradient Coding via the Stochastic Block Model. CoRR abs/1805.10378 (2018) - [i5]Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. CoRR abs/1806.04090 (2018) - [i4]Zachary Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos:
A Geometric Perspective on the Transferability of Adversarial Directions. CoRR abs/1811.03531 (2018) - 2017
- [c1]Alisha Zachariah, Zachary Charles:
Efficiently finding all power flow solutions to tree networks. Allerton 2017: 1107-1114 - [i3]Zachary Charles, Dimitris S. Papailiopoulos:
Stability and Generalization of Learning Algorithms that Converge to Global Optima. CoRR abs/1710.08402 (2017) - [i2]Zachary Charles, Dimitris S. Papailiopoulos, Jordan S. Ellenberg:
Approximate Gradient Coding via Sparse Random Graphs. CoRR abs/1711.06771 (2017) - 2016
- [i1]Zachary Charles:
Generating Random Factored Ideals in Number Fields. CoRR abs/1612.06260 (2016) - 2013
- [j2]Zachary B. Charles, Miriam Farber, Charles R. Johnson, Lee Kennedy-Shaffer:
Nonpositive eigenvalues of the adjacency matrix and lower bounds for Laplacian eigenvalues. Discret. Math. 313(13): 1441-1451 (2013) - [j1]Zachary B. Charles, Miriam Farber, Charles R. Johnson, Lee Kennedy-Shaffer:
Nonpositive Eigenvalues of Hollow, Symmetric, Nonnegative Matrices. SIAM J. Matrix Anal. Appl. 34(3): 1384-1400 (2013)
Coauthor Index
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