Samuel Horvath

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King Abdullah University of Science and Technology

FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

Federated Learning (FL) has been gaining significant traction across dif...
02/26/2021 ∙ by Samuel Horvath, et al. ∙ 0 ∙ share

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Hyperparameter Transfer Learning with Adaptive Complexity

Bayesian optimization (BO) is a sample efficient approach to automatical...
02/25/2021 ∙ by Samuel Horvath, et al. ∙ 18 ∙ share

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Optimal Client Sampling for Federated Learning

It is well understood that client-master communication can be a primary ...
10/26/2020 ∙ by Wenlin Chen, et al. ∙ 6 ∙ share

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Lower Bounds and Optimal Algorithms for Personalized Federated Learning

In this work, we consider the optimization formulation of personalized f...
10/05/2020 ∙ by Filip Hanzely, et al. ∙ 0 ∙ share

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A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning

Modern large-scale machine learning applications require stochastic opti...
06/19/2020 ∙ by Samuel Horvath, et al. ∙ 6 ∙ share

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On Biased Compression for Distributed Learning

In the last few years, various communication compression techniques have...
02/27/2020 ∙ by Aleksandr Beznosikov, et al. ∙ 15 ∙ share

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Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization

Adaptivity is an important yet under-studied property in modern optimiza...
02/13/2020 ∙ by Samuel Horvath, et al. ∙ 4 ∙ share

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Natural Compression for Distributed Deep Learning

Due to their hunger for big data, modern deep learning models are traine...
05/27/2019 ∙ by Samuel Horvath, et al. ∙ 0 ∙ share

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Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop

The stochastic variance-reduced gradient method (SVRG) and its accelerat...
01/24/2019 ∙ by Dmitry Kovalev, et al. ∙ 0 ∙ share

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Samuel Horvath

Featured Co-authors

Michael I. Jordan

Peter Richtarik

Nicholas D. Lane

Stylianos I. Venieris

Filip Hanzely

Cedric Archambeau

Dmitry Kovalev

Ilias Leontiadis

Lihua Lei

Aaron Klein

Marco Canini

FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

Hyperparameter Transfer Learning with Adaptive Complexity

Optimal Client Sampling for Federated Learning

Lower Bounds and Optimal Algorithms for Personalized Federated Learning

A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning

On Biased Compression for Distributed Learning

Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization

Natural Compression for Distributed Deep Learning

Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop