| 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 | 1726 | 2021 |
| Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020 | 293 | 2020 |
| 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 | 210 | 2018 |
| Draco: Byzantine-resilient distributed training via redundant gradients L Chen, H Wang, Z Charles, D Papailiopoulos International Conference on Machine Learning, 903-912, 2018 | 175* | 2018 |
| Stability and generalization of learning algorithms that converge to global optima Z Charles, D Papailiopoulos International conference on machine learning, 745-754, 2018 | 75 | 2018 |
| Approximate gradient coding via sparse random graphs Z Charles, D Papailiopoulos, J Ellenberg arXiv preprint arXiv:1711.06771, 2017 | 62 | 2017 |
| 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 | 59 | 2019 |
| 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 | 34 | 2021 |
| Gradient coding using the stochastic block model Z Charles, D Papailiopoulos 2018 IEEE International Symposium on Information Theory (ISIT), 1998-2002, 2018 | 34* | 2018 |
| 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 | 34 | 1912 |
| Erasurehead: Distributed gradient descent without delays using approximate gradient coding H Wang, Z Charles, D Papailiopoulos arXiv preprint arXiv:1901.09671, 2019 | 32 | 2019 |
| Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al. A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan arXiv preprint arXiv:2107.06917 1 (8), 2021 | 27 | 2021 |
| On the outsized importance of learning rates in local update methods Z Charles, J Konečný arXiv preprint arXiv:2007.00878, 2020 | 27 | 2020 |
| 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 | 22 | 2019 |
| 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 | 16 | 2019 |
| 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 | 14 | 2021 |
| Sparse subspace clustering with missing and corrupted data Z Charles, A Jalali, R Willett 2018 IEEE Data Science Workshop (DSW), 180-184, 2018 | 13 | 2018 |
| 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 | 13 | 2013 |
| Convergence and margin of adversarial training on separable data Z Charles, S Rajput, S Wright, D Papailiopoulos arXiv preprint arXiv:1905.09209, 2019 | 10 | 2019 |
| On large-cohort training for federated learning Z Charles, Z Garrett, Z Huo, S Shmulyian, V Smith Advances in Neural Information Processing Systems 34, 2021 | 9 | 2021 |
Dimitris PapailiopoulosAssistant Professor, University of Wisconsin-MadisonVerified email at papail.io
