Keith Rush

According to our database1, Keith Rush authored at least 13 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2023
(Amplified) Banded Matrix Factorization: A unified approach to private training.
CoRR, 2023

Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning.
CoRR, 2023

Federated Automatic Differentiation.
CoRR, 2023

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.
Proceedings of the International Conference on Machine Learning, 2023

2022
Private Online Prefix Sums via Optimal Matrix Factorizations.
CoRR, 2022

Does Federated Dropout actually work?
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Iterated Vector Fields and Conservatism, with Applications to Federated Learning.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021
Federated Reconstruction: Partially Local Federated Learning.
CoRR, 2021

Adaptive Federated Optimization.
Proceedings of the 9th International Conference on Learning Representations, 2021

(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces.
Proceedings of the Conference on Learning Theory, 2021

2020
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning.
CoRR, 2020

2019
Improving Federated Learning Personalization via Model Agnostic Meta Learning.
CoRR, 2019


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