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ORCIDZachary Charles
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2020 – today
- 2020
[i15]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan:
Adaptive Federated Optimization. CoRR abs/2003.00295 (2020)- [i14]Zachary Charles, Jakub Konecný:
On the Outsized Importance of Learning Rates in Local Update Methods. CoRR abs/2007.00878 (2020)
2010 – 2019
- 2019
- [c10]Zachary B. Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos:
A Geometric Perspective on the Transferability of Adversarial Directions. AISTATS 2019: 1960-1968 - [c9]Shashank Rajput, Zhili Feng, Zachary B. Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? ICML 2019: 5321-5330 - [c8]Shashank Rajput, Hongyi Wang, Zachary B. Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. NeurIPS 2019: 10320-10330 - [i13]Hongyi Wang, Zachary B. Charles, Dimitris S. Papailiopoulos:
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding. CoRR abs/1901.09671 (2019) - [i12]Shashank Rajput, Zhili Feng, Zachary B. Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? CoRR abs/1905.03177 (2019) - [i11]Zachary B. Charles, Shashank Rajput, Stephen Wright, Dimitris S. Papailiopoulos:
Convergence and Margin of Adversarial Training on Separable Data. CoRR abs/1905.09209 (2019) - [i10]Shashank Rajput, Hongyi Wang, Zachary B. Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. CoRR abs/1907.12205 (2019) - [i9]Scott Sievert, Zachary B. Charles:
Improving the convergence of SGD through adaptive batch sizes. CoRR abs/1910.08222 (2019) - [i8]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith 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:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [j4]Zachary Charles
, Nigel Boston:
Exploiting algebraic structure in global optimization and the Belgian chocolate problem. J. Glob. Optim. 72(2): 241-254 (2018) - [j3]Zachary Charles:
Generating random factored ideals in number fields. Math. Comput. 87(312): 2047-2056 (2018) - [c7]Zachary Charles, Amin Jalali, Rebecca Willett:
Sparse Subspace Clustering with Missing and Corrupted Data. DSW 2018: 180-184 - [c6]Zachary B. Charles, Dimitris S. Papailiopoulos:
Stability and Generalization of Learning Algorithms that Converge to Global Optima. ICML 2018: 744-753 - [c5]Lingjiao Chen, Hongyi Wang, Zachary B. Charles, Dimitris S. Papailiopoulos:
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients. ICML 2018: 902-911 - [c4]Alisha Zachariah, Zachary Charles, Nigel Boston, Bernard C. Lesieutre:
Distributions of the Number of Solutions to the Network Power Flow Equations. ISCAS 2018: 1-5 - [c3]Zachary B. Charles, Dimitris S. Papailiopoulos:
Gradient Coding Using the Stochastic Block Model. ISIT 2018: 1998-2002 - [c2]Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary B. Charles, Dimitris S. Papailiopoulos, Stephen Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. NeurIPS 2018: 9872-9883 - [i7]Lingjiao Chen, Hongyi Wang, Zachary B. Charles, Dimitris S. Papailiopoulos:
DRACO: Robust Distributed Training via Redundant Gradients. CoRR abs/1803.09877 (2018) - [i6]Zachary B. Charles, Dimitris S. Papailiopoulos:
Gradient Coding via the Stochastic Block Model. CoRR abs/1805.10378 (2018) - [i5]Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary B. Charles, Dimitris S. Papailiopoulos, Stephen Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. CoRR abs/1806.04090 (2018) - [i4]Zachary B. Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos:
A Geometric Perspective on the Transferability of Adversarial Directions. CoRR abs/1811.03531 (2018) - 2017
- [c1]Alisha Zachariah, Zachary Charles:
Efficiently finding all power flow solutions to tree networks. Allerton 2017: 1107-1114 - [i3]Zachary B. Charles, Dimitris S. Papailiopoulos:
Stability and Generalization of Learning Algorithms that Converge to Global Optima. CoRR abs/1710.08402 (2017) - [i2]Zachary B. Charles, Dimitris S. Papailiopoulos, Jordan S. Ellenberg:
Approximate Gradient Coding via Sparse Random Graphs. CoRR abs/1711.06771 (2017) - 2016
- [i1]Zachary B. Charles:
Generating Random Factored Ideals in Number Fields. CoRR abs/1612.06260 (2016) - 2013
- [j2]Zachary B. Charles, Miriam Farber, Charles R. Johnson, Lee Kennedy-Shaffer:
Nonpositive eigenvalues of the adjacency matrix and lower bounds for Laplacian eigenvalues. Discret. Math. 313(13): 1441-1451 (2013) - [j1]Zachary B. Charles, Miriam Farber, Charles R. Johnson, Lee Kennedy-Shaffer:
Nonpositive Eigenvalues of Hollow, Symmetric, Nonnegative Matrices. SIAM J. Matrix Anal. Appl. 34(3): 1384-1400 (2013)
Coauthor Index
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