Παρακολούθηση
Keith Rush
Keith Rush
Google
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα google.com - Αρχική σελίδα
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Παρατίθεται από
Παρατίθεται από
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Adaptive federated optimization
S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2020
16532020
Improving federated learning personalization via model agnostic meta learning
Y Jiang, J Konečný, K Rush, S Kannan
arXiv preprint arXiv:1909.12488, 2019
6992019
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
1542021
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
68*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
45*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
392022
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
392021
(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
312024
Adaptive Federated Optimization (2020)
S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konecný, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2003
252003
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
232022
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
172023
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
122024
Orthogonal Polynomials on the Circle for the Weight w Satisfying Conditions
S Denisov, K Rush
Constructive Approximation 46, 285-303, 2017
112017
Cascade-Aware Training of Language Models
C Wang, S Augenstein, K Rush, W Jitkrittum, H Narasimhan, AS Rawat, ...
arXiv preprint arXiv:2406.00060, 2024
42024
Iterated Vector Fields and Conservatism, with Applications to Federated Learning
Z Charles, K Rush
https://arxiv.org/abs/2109.03973, 2021
42021
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 …, 2024
3*2024
On Schur parameters in Steklov’s problem
S Denisov, K Rush
Journal of Approximation Theory 215, 68-91, 2017
32017
Federated automatic differentiation
K Rush, Z Charles, Z Garrett
Journal of Machine Learning Research 25 (357), 1-39, 2024
12024
Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch
A Douillard, Y Donchev, K Rush, S Kale, Z Charles, Z Garrett, G Teston, ...
arXiv preprint arXiv:2501.18512, 2025
2025
Randomized Verblunsky parameters in Steklov's problem
K Rush
Journal of Mathematical Analysis and Applications 468 (2), 608-621, 2018
2018
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