Sai Praneeth Karimireddy

Trans. Mach. Learn. Res., 2024

MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images.

[BibT_eX]

[DOI]

Klavdiia Naumova

Arnout Devos

Mary-Anne Hartley

npj Digit. Medicine, 2024

Defection-Free Collaboration between Competitors in a Learning System.

[BibT_eX]

[DOI]

Mariel A. Werner

CoRR, 2024

Data Acquisition via Experimental Design for Decentralized Data Markets.

[BibT_eX]

[DOI]

Charles Lu

Baihe Huang

Praneeth Vepakomma

Ramesh Raskar

CoRR, 2024

Collaborative Heterogeneous Causal Inference Beyond Meta-analysis.

[BibT_eX]

[DOI]

Tianyu Guo

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Privacy Can Arise Endogenously in an Economic System with Learning Agents.

[BibT_eX]

[DOI]

Nivasini Ananthakrishnan

Tiffany Ding

Mariel A. Werner

Proceedings of the 5th Symposium on Foundations of Responsible Computing, 2024

2023

Provably Personalized and Robust Federated Learning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Scaff-PD: Communication Efficient Fair and Robust Federated Learning.

[BibT_eX]

[DOI]

Yaodong Yu

Yi Ma

CoRR, 2023

Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning.

[BibT_eX]

[DOI]

Baihe Huang

CoRR, 2023

Online Learning in a Creator Economy.

[BibT_eX]

[DOI]

Banghua Zhu

Jiantao Jiao

CoRR, 2023

Federated Conformal Predictors for Distributed Uncertainty Quantification.

[BibT_eX]

[DOI]

Charles Lu

Yaodong Yu

Ramesh Raskar

Proceedings of the International Conference on Machine Learning, 2023

Agree to Disagree: Diversity through Disagreement for Better Transferability.

[BibT_eX]

[DOI]

Matteo Pagliardini

François Fleuret

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks.

[BibT_eX]

[DOI]

Narasimha Raghavan Veeraragavan

Severin Elvatun

Jan F. Nygård

Proceedings of the Eighth International Conference on Fog and Mobile Edge Computing, 2023

2022

Mechanisms that Incentivize Data Sharing in Federated Learning.

[BibT_eX]

[DOI]

Wenshuo Guo

CoRR, 2022

Byzantine-Robust Decentralized Learning via Self-Centered Clipping.

[BibT_eX]

[DOI]

CoRR, 2022

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels.

[BibT_eX]

[DOI]

Yaodong Yu

Alexander Wei

Yi Ma

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Towards Model Agnostic Federated Learning Using Knowledge Distillation.

[BibT_eX]

[DOI]

Andrei Afonin

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Optimization methods for collaborative learning.

[BibT_eX]

[DOI]

PhD thesis, 2021

Linear Speedup in Personalized Collaborative Learning.

[BibT_eX]

[DOI]

El Mahdi Chayti

Nicolas Flammarion

CoRR, 2021

Optimal Model Averaging: Towards Personalized Collaborative Learning.

[BibT_eX]

[DOI]

Felix Grimberg

Mary-Anne Hartley

CoRR, 2021

A Field Guide to Federated Optimization.

[BibT_eX]

[DOI]

CoRR, 2021

RelaySum for Decentralized Deep Learning on Heterogeneous Data.

[BibT_eX]

[DOI]

Anastasia Koloskova

Tao Lin

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

Breaking the centralized barrier for cross-device federated learning.

[BibT_eX]

[DOI]

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

Learning from History for Byzantine Robust Optimization.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data.

[BibT_eX]

[DOI]

Tao Lin

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2020

PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Byzantine-Robust Learning on Heterogeneous Datasets via Resampling.

[BibT_eX]

[DOI]

CoRR, 2020

Secure Byzantine-Robust Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Why are Adaptive Methods Good for Attention Models?

[BibT_eX]

[DOI]

Jingzhao Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Practical Low-Rank Communication Compression in Decentralized Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning.

[BibT_eX]

[DOI]

Felix Grimberg

Mary-Anne Hartley

Proceedings of the Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 2020

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Accelerating Gradient Boosting Machines.

[BibT_eX]

[DOI]

Haihao Lu

Natalia Ponomareva

Vahab S. Mirrokni

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Why ADAM Beats SGD for Attention Models.

[BibT_eX]

[DOI]

Jingzhao Zhang

CoRR, 2019

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2019

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication.

[BibT_eX]

[DOI]

CoRR, 2019

Accelerating Gradient Boosting Machine.

[BibT_eX]

[DOI]

Haihao Lu

Natalia Ponomareva

Vahab S. Mirrokni

CoRR, 2019

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization.

[BibT_eX]

[DOI]

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

Error Feedback Fixes SignSGD and other Gradient Compression Schemes.

[BibT_eX]

[DOI]

Quentin Rebjock

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

Efficient Greedy Coordinate Descent for Composite Problems.

[BibT_eX]

[DOI]

Anastasia Koloskova

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

2018

Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients.

[BibT_eX]

[DOI]

CoRR, 2018

Revisiting First-Order Convex Optimization Over Linear Spaces.

[BibT_eX]

[DOI]

Francesco Locatello

Anant Raj

CoRR, 2018

On Matching Pursuit and Coordinate Descent.

[BibT_eX]

[DOI]

Francesco Locatello

Anant Raj

Sai Praneeth Reddy Karimireddy

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

Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems.

[BibT_eX]

[DOI]