| Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečnę, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020 | 1418 | 2020 |
| Improving federated learning personalization via model agnostic meta learning Y Jiang, J Konečnę, K Rush, S Kannan arXiv preprint arXiv:1909.12488, 2019 | 635 | 2019 |
| Federated reconstruction: Partially local federated learning K Singhal, H Sidahmed, Z Garrett, S Wu, J Rush, S Prakash Advances in Neural Information Processing Systems 34, 11220-11232, 2021 | 140 | 2021 |
| Improved differential privacy for sgd via optimal private linear operators on adaptive streams S Denisov, HB McMahan, J Rush, A Smith, A Guha Thakurta Advances in Neural Information Processing Systems 35, 5910-5924, 2022 | 57* | 2022 |
| (Nearly) Dimension Independent Private ERM with AdaGrad Rates\{via Publicly Estimated Subspaces P Kairouz, MR Diaz, K Rush, A Thakurta Conference on Learning Theory, 2717-2746, 2021 | 41* | 2021 |
| Differentially private model personalization P Jain, J Rush, A Smith, S Song, A Guha Thakurta Advances in neural information processing systems 34, 29723-29735, 2021 | 36 | 2021 |
| Multi-epoch matrix factorization mechanisms for private machine learning CA Choquette-Choo, HB McMahan, K Rush, A Thakurta arXiv preprint arXiv:2211.06530, 2022 | 28 | 2022 |
| (Amplified) Banded Matrix Factorization: A unified approach to private training CA Choquette-Choo, A Ganesh, R McKenna, HB McMahan, J Rush, ... Advances in Neural Information Processing Systems 36, 2024 | 20 | 2024 |
| Does federated dropout actually work? G Cheng, Z Charles, Z Garrett, K Rush Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 17 | 2022 |
| Orthogonal Polynomials on the Circle for the Weight w Satisfying Conditions S Denisov, K Rush Constructive Approximation 46, 285-303, 2017 | 11 | 2017 |
| Gradient descent with linearly correlated noise: Theory and applications to differential privacy A Koloskova, R McKenna, Z Charles, J Rush, HB McMahan Advances in Neural Information Processing Systems 36, 2023 | 10 | 2023 |
| Iterated Vector Fields and Conservatism, with Applications to Federated Learning Z Charles, K Rush https://arxiv.org/abs/2109.03973, 2021 | 4 | 2021 |
| On Schur parameters in Steklov’s problem S Denisov, K Rush Journal of Approximation Theory 215, 68-91, 2017 | 3 | 2017 |
| Fine-Tuning Large Language Models with User-Level Differential Privacy Z Charles, A Ganesh, R McKenna, HB McMahan, N Mitchell, K Pillutla, ... arXiv preprint arXiv:2407.07737, 2024 | 1 | 2024 |
| FAX: Scalable and Differentiable Federated Primitives in JAX K Rush, Z Charles, Z Garrett arXiv preprint arXiv:2403.07128, 2024 | 1 | 2024 |
| Federated Automatic Differentiation K Rush, Z Charles, Z Garrett arXiv preprint arXiv:2301.07806, 2023 | 1 | 2023 |
| Cascade-Aware Training of Language Models C Wang, S Augenstein, K Rush, W Jitkrittum, H Narasimhan, AS Rawat, ... arXiv preprint arXiv:2406.00060, 2024 | | 2024 |
| Randomized Verblunsky parameters in Steklov's problem K Rush Journal of Mathematical Analysis and Applications 468 (2), 608-621, 2018 | | 2018 |
| Orthogonal polynomials on the unit circle: Steklov problems and weight perturbations K Rush The University of Wisconsin-Madison, 2016 | | 2016 |
| DrJAX: Scalable and Differentiable MapReduce Primitives in JAX JK Rush, Z Charles, Z Garrett, S Augenstein, NE Mitchell 2nd Workshop on Advancing Neural Network Training: Computational Efficiency …, 0 | | |
Zachary CharlesResearch Scientist, GoogleVerified email at google.com
