Nonpositive eigenvalues of the adjacency matrix and lower bounds for Laplacian eigenvalues. - Zachary B. Charles; Miriam Farber; Charles R. Johnson; Lee Kennedy-Shaffer (Discret. Math., 2013)

Nonpositive Eigenvalues of Hollow, Symmetric, Nonnegative Matrices. - Zachary B. Charles; Miriam Farber; Charles R. Johnson; Lee Kennedy-Shaffer (SIAM J. Matrix Anal. Appl., 2013)

Efficiently finding all power flow solutions to tree networks. - Alisha Zachariah; Zachary Charles (Allerton, 2017)

Approximate Gradient Coding via Sparse Random Graphs. - Zachary Charles; Dimitris S. Papailiopoulos; Jordan S. Ellenberg (CoRR, 2017)

Sparse Subspace Clustering with Missing and Corrupted Data. - Zachary Charles; Amin Jalali 002; Rebecca Willett (DSW, 2018)

Stability and Generalization of Learning Algorithms that Converge to Global Optima. - Zachary Charles; Dimitris S. Papailiopoulos (ICML, 2018)

DRACO: Byzantine-resilient Distributed Training via Redundant Gradients. - Lingjiao Chen; Hongyi Wang 001; Zachary Charles; Dimitris S. Papailiopoulos (ICML, 2018)

Distributions of the Number of Solutions to the Network Power Flow Equations. - Alisha Zachariah; Zachary Charles; Nigel Boston; Bernard C. Lesieutre (ISCAS, 2018)

Gradient Coding Using the Stochastic Block Model. - Zachary Charles; Dimitris S. Papailiopoulos (ISIT, 2018)

ATOMO: Communication-efficient Learning via Atomic Sparsification. - Hongyi Wang 001; Scott Sievert; Shengchao Liu; Zachary Charles; Dimitris S. Papailiopoulos; Stephen J. Wright 001 (NeurIPS, 2018)

DRACO: Robust Distributed Training via Redundant Gradients. - Lingjiao Chen; Hongyi Wang 001; Zachary Charles; Dimitris S. Papailiopoulos (CoRR, 2018)

Gradient Coding via the Stochastic Block Model. - Zachary Charles; Dimitris S. Papailiopoulos (CoRR, 2018)

Exploiting algebraic structure in global optimization and the Belgian chocolate problem. - Zachary Charles; Nigel Boston (J. Glob. Optim., 2018)

Generating random factored ideals in number fields. - Zachary Charles (Math. Comput., 2018)

A Geometric Perspective on the Transferability of Adversarial Directions. - Zachary Charles; Harrison Rosenberg; Dimitris S. Papailiopoulos (AISTATS, 2019)

Does Data Augmentation Lead to Positive Margin? - Shashank Rajput; Zhili Feng; Zachary Charles; Po-Ling Loh; Dimitris S. Papailiopoulos (ICML, 2019)

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. - Shashank Rajput; Hongyi Wang 001; Zachary Charles; Dimitris S. Papailiopoulos (NeurIPS, 2019)

ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding. - Hongyi Wang 001; Zachary Charles; Dimitris S. Papailiopoulos (CoRR, 2019)

Convergence and Margin of Adversarial Training on Separable Data. - Zachary Charles; Shashank Rajput; Stephen J. Wright 001; Dimitris S. Papailiopoulos (CoRR, 2019)

Improving the convergence of SGD through adaptive batch sizes. - Scott Sievert; Zachary Charles (CoRR, 2019)

Advances and Open Problems in Federated Learning. - 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; et al. (CoRR, 2019)

On the Outsized Importance of Learning Rates in Local Update Methods. - Zachary Charles; Jakub Konecný (CoRR, 2020)

Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. - Zachary Charles; Jakub Konecný (AISTATS, 2021)

Adaptive Federated Optimization. - Sashank J. Reddi; Zachary Charles; Manzil Zaheer; Zachary Garrett; Keith Rush; Jakub Konecný; Sanjiv Kumar; Hugh Brendan McMahan (ICLR, 2021)

On Large-Cohort Training for Federated Learning. - Zachary Charles; Zachary Garrett; Zhouyuan Huo; Sergei Shmulyian; Virginia Smith (NeurIPS, 2021)

Local Adaptivity in Federated Learning: Convergence and Consistency. - Jianyu Wang; Zheng Xu 002; Zachary Garrett; Zachary Charles; Luyang Liu; Gauri Joshi (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Advances and Open Problems in Federated Learning. - 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; et al. (Found. Trends Mach. Learn., 2021)

Iterated Vector Fields and Conservatism, with Applications to Federated Learning. - Zachary Charles; Keith Rush (ALT, 2022)

Does Federated Dropout actually work? - Gary Cheng 004; Zachary Charles; Zachary Garrett; Keith Rush (CVPR Workshops, 2022)

Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory. - Nicole Mitchell; Johannes Ballé; Zachary Charles; Jakub Konecný (CoRR, 2022)

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. - Shanshan Wu; Tian Li 005; Zachary Charles; Yu Xiao; Ken Ziyu Liu; Zheng Xu 002; Virginia Smith (CoRR, 2022)

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning. - Zachary Charles; Kallista A. Bonawitz; Stanislav Chiknavaryan; Brendan McMahan; Blaise Agüera y Arcas (CoRR, 2022)

A Rate-Distortion View on Model Updates. - Nicole Mitchell; Johannes Ballé; Zachary Charles; Jakub Konecný (Tiny Papers @ ICLR, 2023)

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. - Zachary Charles; Nicole Mitchell; Krishna Pillutla; Michael Reneer; Zachary Garrett (NeurIPS, 2023)

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. - Anastasia Koloskova; Ryan McKenna; Zachary Charles; John Keith Rush; H. Brendan McMahan (NeurIPS, 2023)

Federated Automatic Differentiation. - Keith Rush; Zachary Charles; Zachary Garrett (CoRR, 2023)

Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. - Anastasia Koloskova; Ryan McKenna; Zachary Charles; Keith Rush; Brendan McMahan (CoRR, 2023)

FAX: Scalable and Differentiable Federated Primitives in JAX. - Keith Rush; Zachary Charles; Zachary Garrett (CoRR, 2024)

Fine-Tuning Large Language Models with User-Level Differential Privacy. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (CoRR, 2024)

Leveraging Function Space Aggregation for Federated Learning at Scale. - Nikita Dhawan; Nicole Mitchell; Zachary Charles; Zachary Garrett; Gintare Karolina Dziugaite (Trans. Mach. Learn. Res., 2024)

Learning with User-Level Differential Privacy Under Fixed Compute Budgets. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (SaTML, 2025)

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch. - Arthur Douillard; Yanislav Donchev; Keith Rush; Satyen Kale; Zachary Charles; Zachary Garrett; Gabriel Teston; Dave Lacey; Ross McIlroy; Jiajun Shen; Alexandre Ramé; Arthur Szlam; et al. (CoRR, 2025)

Scaling Laws for Differentially Private Language Models. - Ryan McKenna; Yangsibo Huang; Amer Sinha; Borja Balle; Zachary Charles; Christopher A. Choquette-Choo; Badih Ghazi; Georgios Kaissis; Ravi Kumar 001; Ruibo Liu; Da Yu; Chiyuan Zhang (CoRR, 2025)

Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo. - Zachary Charles; Gabriel Teston; Lucio Dery; Keith Rush; Nova Fallen; Zachary Garrett; Arthur Szlam; Arthur Douillard (CoRR, 2025)