| 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 | 4699 | 2020 |
| On the convergence of adam and beyond SJ Reddi, S Kale, S Kumar arXiv preprint arXiv:1904.09237, 2019 | 3834 | 2019 |
| Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020 | 2268 | 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 | 1394 | 2019 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| Stochastic variance reduction for nonconvex optimization SJ Reddi, A Hefny, S Sra, B Poczos, A Smola International conference on machine learning, 314-323, 2016 | 774 | 2016 |
| Adaptive methods for nonconvex optimization M Zaheer, S Reddi, D Sachan, S Kale, S Kumar Advances in neural information processing systems 31, 2018 | 629 | 2018 |
| 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 | 564 | 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 | 516 | 2021 |
| 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 | 389 | 2020 |
| 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 | 332 | 2019 |
| Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds H Zhang, S J Reddi, S Sra Advances in Neural Information Processing Systems 29, 2016 | 330 | 2016 |
| Fast Stochastic Methods for Nonsmooth Nonconvex Optimization S J. Reddi, S Sra, B Poczos, A Smola arXiv:1605.06900, 2016 | 296* | 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 | 277 | 2020 |
| 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 | 234 | 2015 |
| Can gradient clipping mitigate label noise? AK Menon, AS Rawat, SJ Reddi, S Kumar International conference on learning representations, 2020 | 222 | 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 | 216 | 2015 |
| 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 | 204 | 2016 |
| Aide: Fast and communication efficient distributed optimization SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola arXiv preprint arXiv:1608.06879, 2016 | 194 | 2016 |
| Adaclip: Adaptive clipping for private sgd V Pichapati, AT Suresh, FX Yu, SJ Reddi, S Kumar arXiv preprint arXiv:1908.07643, 2019 | 171 | 2019 |
Sanjiv KumarGoogle Fellow, VP, Google DeepMindVerified email at google.com
