I am a research scientist at Google working on federated learning. I received a Ph.D. in applied mathematics from the University of Wisconsin-Madison, and went on to do a postdoc with the wonderful Dimitris Papailiopoulos.
I am interested in practical and efficient optimization methods for machine learning, especially in distributed settings. I also work on open-source libraries for federated learning experimentation, including TensorFlow Federated and Dataset Grouper.
Publications & Preprints
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
Z. Charles, N. Mitchell, K. Pillutla, M. Reneer, Z. Garrett. To appear, NeurIPS 2023.
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning
A. Koloskova, R. McKenna, Z. Charles, K. Rush, B. McMahan. To appear, NeurIPS 2023.
A Rate-Distortion View on Model Updates
N. Mitchell, J. Ballé, Z. Charles, J. Konečný. ICLR Tiny Papers, 2023.
Federated Automatic Differentiation
K. Rush, Z. Charles, Z. Garrett.
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
Z. Charles, K. Bonawitz, S. Chiknavaryan, B. McMahan, B. Agüera y Arcas.
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
S. Wu, T. Li, Z. Charles, Y. Xiao, Z. Liu, Z. Xu, V. Smith.
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
N. Mitchell, J. Ballé, Z. Charles, J. Konečný.
Iterated Vector Fields and Conservatism, with Applications to Federated Learning
Z. Charles, K. Rush. ALT 2022.
A Field Guide to Federated Optimization
J. Wang, Z. Charles, Z. Xu, G. Joshi, H. B. McMahan, et al.
On Large-Cohort Training for Federated Learning
Z. Charles, Z. Garrett, Z. Huo, S. Shmulyian, V. Smith. NeurIPS 2021.
Local Adaptivity in Federated Learning: Convergence and Consistency
J. Wang, Z. Xu, Z. Garrett, Z. Charles, L. Liu, G. Joshi.
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
Z. Charles and J. Konečný. AISTATS 2021.
Adaptive Federated Optimization
S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný, S. Kumar, H. B. McMahan. ICLR 2021.
Advances and Open Problems in Federated Learning
P. Kairouz, H. B. McMahan, et al. (including Z. Charles).
On the Outsized Importance of Learning Rates in Local Update Methods
Z. Charles and J. Konečný.
Convergence and Margin of Adversarial Training on Separable Data
Z. Charles, S. Rajput, S. Wright, D. Papailiopoulos.
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation (arXiv)
S. Rajput, H. Wang, Z. Charles, D. Papailiopoulos. NeurIPS 2019.
A Geometric Perspective on the Transferability of Adversarial Directions (arXiv)
Z. Charles, H. Rosenberg, D. Papailiopoulos. AISTATS, 2019.
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Codes
H. Wang, Z. Charles, D. Papailiopoulos.
ATOMO: Communication-efficient Learning via Atomic Sparsification (arXiv)
H. Wang, S. Sievert, Z. Charles, S. Liu, S. Wright, D. Papailiopoulos. NeurIPS, 2018.
Stability and Generalization of Learning Algorithms that Converge to Global Optima (arXiv)
Z. Charles and D. Papailiopoulos. ICML, 2018.
Approximate Gradient Coding via Sparse Random Graphs (arXiv)
Z. Charles, D. Papailiopoulos, J. Ellenberg.
DRACO: Robust Distributed Training via Redundant Gradients (arXiv)
L. Chen, H. Wang, Z. Charles, D. Papailiopoulos. ICML, 2018.
Subspace Clustering with Missing and Corrupted Data (arXiv)
Z. Charles, A. Jalali, R. Willett. IEEE Data Science Workshop, 2018.
Exploiting Algebraic Structure in Global Optimization and the Belgian Chocolate Problem (arXiv)
Z. Charles and N. Boston. Journal of Global Optimization, 2018.
Generating Random Factored Ideals in Number Fields (arXiv)
Z. Charles. Mathematics of Computation, 2018.
2017 and earlier
Algebraic and Geometric Structure in Machine Learning and Optimization Algorithms (link)
Z. Charles. Ph.D. Thesis, University of Wisconsin-Madison, Dec 2017.
Efficiently Finding All Power Flow Solutions to Tree Networks
A. Zachariah and Z. Charles. Allerton, 2017.
Nonpositive Eigenvalues of Hollow, Symmetric, Nonnegative Matrices (arXiv)
Z. Charles, M. Farber, C. R. Johnson, L. Kennedy-Shaffer. SIAM Journal on Matrix Analysis and Applications, 2013.
Nonpositive Eigenvalues of the Adjacency Matrix and Lower Bounds for Laplacian Eigenvalues (arXiv)
Z. Charles, M. Farber, C. R. Johnson, L. Kennedy-Shaffer. Discrete Mathematics, 2013.
The Relation Between the Diagonal Entries and the Eigenvalues of a Symmetric Matrix, Based upon the Sign Pattern of its Off-Diagonal Entries
Z. Charles, M. Farber, C. R. Johnson, L. Kennedy-Shaffer. Linear Algebra and its Applications, 2013.