Zachary Charles

Gintare Karolina Dziugaite

Zachary Garrett

CoRR, 2024

2023

Leveraging Function Space Aggregation for Federated Learning at Scale.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Federated Automatic Differentiation.

[BibT_eX]

[DOI]

Keith Rush

Zachary Garrett

CoRR, 2023

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Rate-Distortion View on Model Updates.

[BibT_eX]

[DOI]

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

2022

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning.

[BibT_eX]

[DOI]

Kallista A. Bonawitz

Stanislav Chiknavaryan

Brendan McMahan

Blaise Agüera y Arcas

CoRR, 2022

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory.

[BibT_eX]

[DOI]

CoRR, 2022

Does Federated Dropout actually work?

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Iterated Vector Fields and Conservatism, with Applications to Federated Learning.

[BibT_eX]

[DOI]

Keith Rush

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021

Advances and Open Problems in Federated Learning.

[BibT_eX]

[DOI]

Rafael G. L. D'Oliveira

Ananda Theertha Suresh

Found. Trends Mach. Learn., 2021

A Field Guide to Federated Optimization.

[BibT_eX]

[DOI]

CoRR, 2021

Local Adaptivity in Federated Learning: Convergence and Consistency.

[BibT_eX]

[DOI]

CoRR, 2021

On Large-Cohort Training for Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adaptive Federated Optimization.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning.

[BibT_eX]

[DOI]

Jakub Konecný

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On the Outsized Importance of Learning Rates in Local Update Methods.

[BibT_eX]

[DOI]

Jakub Konecný

CoRR, 2020

2019

Advances and Open Problems in Federated Learning.

[BibT_eX]

[DOI]

Rafael G. L. D'Oliveira

Ananda Theertha Suresh

CoRR, 2019

Improving the convergence of SGD through adaptive batch sizes.

[BibT_eX]

[DOI]

Scott Sievert

CoRR, 2019

Convergence and Margin of Adversarial Training on Separable Data.

[BibT_eX]

[DOI]

Shashank Rajput

Stephen J. Wright

CoRR, 2019

ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding.

[BibT_eX]

[DOI]

CoRR, 2019

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation.

[BibT_eX]

[DOI]

Shashank Rajput

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Does Data Augmentation Lead to Positive Margin?

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

A Geometric Perspective on the Transferability of Adversarial Directions.

[BibT_eX]

[DOI]

Harrison Rosenberg

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Generating random factored ideals in number fields.

[BibT_eX]

[DOI]

Math. Comput., 2018

Exploiting algebraic structure in global optimization and the Belgian chocolate problem.

[BibT_eX]

[DOI]

Nigel Boston

J. Glob. Optim., 2018

Gradient Coding via the Stochastic Block Model.

[BibT_eX]

[DOI]

CoRR, 2018

DRACO: Robust Distributed Training via Redundant Gradients.

[BibT_eX]

[DOI]

Lingjiao Chen

CoRR, 2018

ATOMO: Communication-efficient Learning via Atomic Sparsification.

[BibT_eX]

[DOI]

Stephen J. Wright

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Gradient Coding Using the Stochastic Block Model.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Distributions of the Number of Solutions to the Network Power Flow Equations.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Circuits and Systems, 2018

DRACO: Byzantine-resilient Distributed Training via Redundant Gradients.

[BibT_eX]

[DOI]

Lingjiao Chen

Proceedings of the 35th International Conference on Machine Learning, 2018

Stability and Generalization of Learning Algorithms that Converge to Global Optima.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

Sparse Subspace Clustering with Missing and Corrupted Data.

[BibT_eX]

[DOI]

Amin Jalali

Rebecca Willett

Proceedings of the 2018 IEEE Data Science Workshop, 2018

2017

Approximate Gradient Coding via Sparse Random Graphs.

[BibT_eX]

[DOI]

Jordan S. Ellenberg

CoRR, 2017

Efficiently finding all power flow solutions to tree networks.

[BibT_eX]

[DOI]

Alisha Zachariah