| On the convergence of adam and beyond SJ Reddi, S Kale, S Kumar arXiv preprint arXiv:1904.09237, 2019 | 3312 | 2019 |
| Scaffold: Stochastic controlled averaging for federated learning SP Karimireddy, S Kale, M Mohri, S Reddi, S Stich, AT Suresh International conference on machine learning, 5132-5143, 2020 | 3212 | 2020 |
| Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020 | 1632 | 2020 |
| Large batch optimization for deep learning: Training bert in 76 minutes Y You, J Li, S Reddi, J Hseu, S Kumar, S Bhojanapalli, X Song, J Demmel, ... arXiv preprint arXiv:1904.00962, 2019 | 1104 | 2019 |
| Stochastic variance reduction for nonconvex optimization SJ Reddi, A Hefny, S Sra, B Poczos, A Smola International conference on machine learning, 314-323, 2016 | 699 | 2016 |
| Adaptive methods for nonconvex optimization M Zaheer, S Reddi, D Sachan, S Kale, S Kumar Advances in neural information processing systems 31, 2018 | 536 | 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 | 398 | 2021 |
| Are transformers universal approximators of sequence-to-sequence functions? C Yun, S Bhojanapalli, AS Rawat, SJ Reddi, S Kumar arXiv preprint arXiv:1912.10077, 2019 | 380 | 2019 |
| Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds H Zhang, S J Reddi, S Sra Advances in Neural Information Processing Systems 29, 2016 | 295 | 2016 |
| Scaffold: Stochastic controlled averaging for on-device federated learning SP Karimireddy, S Kale, M Mohri, SJ Reddi, SU Stich, AT Suresh arXiv preprint arXiv:1910.06378 2 (6), 2019 | 292 | 2019 |
| Why are adaptive methods good for attention models? J Zhang, SP Karimireddy, A Veit, S Kim, S Reddi, S Kumar, S Sra Advances in Neural Information Processing Systems 33, 15383-15393, 2020 | 271 | 2020 |
| Fast Stochastic Methods for Nonsmooth Nonconvex Optimization S J. Reddi, S Sra, B Poczos, A Smola arXiv:1605.06900, 2016 | 266* | 2016 |
| Mime: Mimicking centralized stochastic algorithms in federated learning SP Karimireddy, M Jaggi, S Kale, M Mohri, SJ Reddi, SU Stich, AT Suresh arXiv preprint arXiv:2008.03606, 2020 | 226 | 2020 |
| On variance reduction in stochastic gradient descent and its asynchronous variants SJ Reddi, A Hefny, S Sra, B Poczos, AJ Smola Advances in neural information processing systems, 2647-2655, 2015 | 211 | 2015 |
| On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions A Ramdas, SJ Reddi, B Póczos, A Singh, L Wasserman Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 210 | 2015 |
| Aide: Fast and communication efficient distributed optimization SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola arXiv preprint arXiv:1608.06879, 2016 | 184 | 2016 |
| Can gradient clipping mitigate label noise? AK Menon, AS Rawat, SJ Reddi, S Kumar International Conference on Learning Representations, 2020 | 181 | 2020 |
| Stochastic frank-wolfe methods for nonconvex optimization SJ Reddi, S Sra, B Póczos, A Smola 2016 54th annual Allerton conference on communication, control, and …, 2016 | 174 | 2016 |
| Adaclip: Adaptive clipping for private sgd V Pichapati, AT Suresh, FX Yu, SJ Reddi, S Kumar arXiv preprint arXiv:1908.07643, 2019 | 145 | 2019 |
| Variance reduction in stochastic gradient Langevin dynamics KA Dubey, S J Reddi, SA Williamson, B Poczos, AJ Smola, EP Xing Advances in neural information processing systems 29, 2016 | 112 | 2016 |
Sanjiv KumarGoogle Fellow, VP, Google Research在 google.com 的电子邮件经过验证
