Publications
Preprints
- On Large-Cohort Training for Federated Learning
Z. Charles, Z. Garrett, Z. Huo, S. Shmulyian, V. Smith.
2021
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.
2020
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.
2019
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.Does Data Augmentation Lead to Positive Margin? (arXiv)
S. Rajput, Z. Feng, Z. Charles, P. Loh, D. Papailiopoulos. ICML, 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.
2018
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.Gradient Coding Using the Stochastic Block Model (arXiv)
Z. Charles and D. Papailiopoulos. ISIT, 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.