Zachary Charles

58 papers A* 10A 2B 2C 1Journal 38Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo. CoRR Zachary Charles, Gabriel Teston, Lucio Dery, Keith Rush, Nova Fallen, Zachary Garrett, Arthur Szlam, Arthur Douillard
2025	—	`conf`	Learning with User-Level Differential Privacy Under Fixed Compute Budgets. SaTML Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
2025	A*	`conf`	Scaling Laws for Differentially Private Language Models. ICML Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang
2025	J	`jnl`	Scaling Laws for Differentially Private Language Models. CoRR Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang
2025	J	`jnl`	Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch. CoRR Arthur Douillard, Yanislav Donchev, Keith Rush, Satyen Kale, Zachary Charles, Zachary Garrett, Gabriel Teston, Dave Lacey, Ross McIlroy, Jiajun Shen, Alexandre Ramé, Arthur Szlam, Marc'Aurelio Ranzato, Paul Barham
2025	J	`jnl`	VaultGemma: A Differentially Private Gemma Model. CoRR Amer Sinha, Thomas Mesnard, Ryan McKenna, Daogao Liu, Christopher A. Choquette-Choo, Yangsibo Huang, Da Yu, Georgios Kaissis, Zachary Charles, Ruibo Liu, Lynn Chua, Pritish Kamath, Pasin Manurangsi, Steve He, Chiyuan Zhang, Badih Ghazi, Borja de Balle Pigem, Prem Eruvbetine, Tris Warkentin, Armand Joulin, Ravi Kumar
2024	J	`jnl`	FAX: Scalable and Differentiable Federated Primitives in JAX. CoRR Keith Rush, Zachary Charles, Zachary Garrett
2024	J	`jnl`	Federated Automatic Differentiation. J. Mach. Learn. Res. Keith Rush, Zachary Charles, Zachary Garrett
2024	J	`jnl`	Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
2024	J	`jnl`	Leveraging Function Space Aggregation for Federated Learning at Scale. Trans. Mach. Learn. Res. Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
2023	—	`conf`	A Rate-Distortion View on Model Updates. Tiny Papers @ ICLR Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný
2023	J	`jnl`	Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan
2023	J	`jnl`	Federated Automatic Differentiation. CoRR Keith Rush, Zachary Charles, Zachary Garrett
2023	A*	`conf`	Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan
2023	J	`jnl`	Leveraging Function Space Aggregation for Federated Learning at Scale. CoRR Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
2023	A*	`conf`	Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. NeurIPS Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett
2023	J	`jnl`	Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. CoRR Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett
2022	—	`conf`	Does Federated Dropout actually work? CVPR Workshops Gary Cheng, Zachary Charles, Zachary Garrett, Keith Rush
2022	J	`jnl`	Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning. CoRR Zachary Charles, Kallista A. Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Agüera y Arcas
2022	B	`conf`	Iterated Vector Fields and Conservatism, with Applications to Federated Learning. ALT Zachary Charles, Keith Rush
2022	J	`jnl`	Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith
2022	J	`jnl`	Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory. CoRR Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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
2021	A*	`conf`	Adaptive Federated Optimization. ICLR Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	A	`conf`	Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. AISTATS Zachary Charles, Jakub Konecný
2021	J	`jnl`	Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. CoRR Zachary Charles, Jakub Konecný
2021	J	`jnl`	Iterated Vector Fields and Conservatism, with Applications to Federated Learning. CoRR Zachary Charles, Keith Rush
2021	J	`jnl`	Local Adaptivity in Federated Learning: Convergence and Consistency. CoRR Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, Gauri Joshi
2021	A*	`conf`	On Large-Cohort Training for Federated Learning. NeurIPS Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
2021	J	`jnl`	On Large-Cohort Training for Federated Learning. CoRR Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
2020	J	`jnl`	Adaptive Federated Optimization. CoRR Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan
2020	J	`jnl`	On the Outsized Importance of Learning Rates in Local Update Methods. CoRR Zachary Charles, Jakub Konecný
2019	A	`conf`	A Geometric Perspective on the Transferability of Adversarial Directions. AISTATS Zachary Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	J	`jnl`	Convergence and Margin of Adversarial Training on Separable Data. CoRR Zachary Charles, Shashank Rajput, Stephen J. Wright, Dimitris S. Papailiopoulos
2019	A*	`conf`	DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. NeurIPS Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos
2019	J	`jnl`	DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. CoRR Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos
2019	A*	`conf`	Does Data Augmentation Lead to Positive Margin? ICML Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos
2019	J	`jnl`	Does Data Augmentation Lead to Positive Margin? CoRR Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos
2019	J	`jnl`	ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding. CoRR Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos
2019	J	`jnl`	Improving the convergence of SGD through adaptive batch sizes. CoRR Scott Sievert, Zachary Charles
2018	J	`jnl`	A Geometric Perspective on the Transferability of Adversarial Directions. CoRR Zachary Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos
2018	A*	`conf`	ATOMO: Communication-efficient Learning via Atomic Sparsification. NeurIPS Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright
2018	J	`jnl`	ATOMO: Communication-efficient Learning via Atomic Sparsification. CoRR Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright
2018	A*	`conf`	DRACO: Byzantine-resilient Distributed Training via Redundant Gradients. ICML Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos
2018	J	`jnl`	DRACO: Robust Distributed Training via Redundant Gradients. CoRR Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos
2018	C	`conf`	Distributions of the Number of Solutions to the Network Power Flow Equations. ISCAS Alisha Zachariah, Zachary Charles, Nigel Boston, Bernard C. Lesieutre
2018	J	`jnl`	Exploiting algebraic structure in global optimization and the Belgian chocolate problem. J. Glob. Optim. Zachary Charles, Nigel Boston
2018	J	`jnl`	Generating random factored ideals in number fields. Math. Comput. Zachary Charles
2018	B	`conf`	Gradient Coding Using the Stochastic Block Model. ISIT Zachary Charles, Dimitris S. Papailiopoulos
2018	J	`jnl`	Gradient Coding via the Stochastic Block Model. CoRR Zachary Charles, Dimitris S. Papailiopoulos
2018	—	`conf`	Sparse Subspace Clustering with Missing and Corrupted Data. DSW Zachary Charles, Amin Jalali, Rebecca Willett
2018	A*	`conf`	Stability and Generalization of Learning Algorithms that Converge to Global Optima. ICML Zachary Charles, Dimitris S. Papailiopoulos
2017	J	`jnl`	Approximate Gradient Coding via Sparse Random Graphs. CoRR Zachary Charles, Dimitris S. Papailiopoulos, Jordan S. Ellenberg
2017	—	`conf`	Efficiently finding all power flow solutions to tree networks. Allerton Alisha Zachariah, Zachary Charles
2017	J	`jnl`	Stability and Generalization of Learning Algorithms that Converge to Global Optima. CoRR Zachary Charles, Dimitris S. Papailiopoulos
2016	J	`jnl`	Generating Random Factored Ideals in Number Fields. CoRR Zachary Charles