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Zachary Charles
Zachary Charles
Research Scientist, Google
google.com의 이메일 확인됨 - 홈페이지
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Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
Foundations and Trends® in Machine Learning 14 (1–2), 1-210, 2021
20942021
Adaptive federated optimization
S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2020
3792020
Atomo: Communication-efficient learning via atomic sparsification
H Wang, S Sievert, S Liu, Z Charles, D Papailiopoulos, S Wright
Advances in Neural Information Processing Systems 31, 2018
2342018
Draco: Byzantine-resilient distributed training via redundant gradients
L Chen, H Wang, Z Charles, D Papailiopoulos
International Conference on Machine Learning, 903-912, 2018
189*2018
A Field Guide to Federated Optimization
J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ...
arXiv preprint arXiv:2107.06917, 2021
962021
Stability and generalization of learning algorithms that converge to global optima
Z Charles, D Papailiopoulos
International Conference on Machine Learning, 745-754, 2018
872018
DETOX: A redundancy-based framework for faster and more robust gradient aggregation
S Rajput, H Wang, Z Charles, D Papailiopoulos
Advances in Neural Information Processing Systems 32, 2019
692019
Approximate gradient coding via sparse random graphs
Z Charles, D Papailiopoulos, J Ellenberg
arXiv preprint arXiv:1711.06771, 2017
662017
Advances and open problems in federated learning. arXiv 2019
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
arXiv preprint arXiv:1912.04977, 1912
381912
Erasurehead: Distributed gradient descent without delays using approximate gradient coding
H Wang, Z Charles, D Papailiopoulos
arXiv preprint arXiv:1901.09671, 2019
352019
Gradient coding using the stochastic block model
Z Charles, D Papailiopoulos
2018 IEEE International Symposium on Information Theory (ISIT), 1998-2002, 2018
35*2018
On the outsized importance of learning rates in local update methods
Z Charles, J Konečný
arXiv preprint arXiv:2007.00878, 2020
312020
Does data augmentation lead to positive margin?
S Rajput, Z Feng, Z Charles, PL Loh, D Papailiopoulos
International Conference on Machine Learning, 5321-5330, 2019
262019
Convergence and accuracy trade-offs in federated learning and meta-learning
Z Charles, J Konečný
International Conference on Artificial Intelligence and Statistics, 2575-2583, 2021
202021
On large-cohort training for federated learning
Z Charles, Z Garrett, Z Huo, S Shmulyian, V Smith
Advances in neural information processing systems 34, 20461-20475, 2021
172021
A geometric perspective on the transferability of adversarial directions
Z Charles, H Rosenberg, D Papailiopoulos
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
172019
Convergence and margin of adversarial training on separable data
Z Charles, S Rajput, S Wright, D Papailiopoulos
arXiv preprint arXiv:1905.09209, 2019
132019
Sparse subspace clustering with missing and corrupted data
Z Charles, A Jalali, R Willett
2018 IEEE Data Science Workshop (DSW), 180-184, 2018
132018
Nonpositive eigenvalues of hollow, symmetric, nonnegative matrices
ZB Charles, M Farber, CR Johnson, L Kennedy-Shaffer
SIAM Journal on Matrix Analysis and Applications 34 (3), 1384-1400, 2013
122013
Distributions of the number of solutions to the network power flow equations
A Zachariah, Z Charles, N Boston, B Lesieutre
2018 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2018
92018
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