Samuel Horvath | DeepAI

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∙ 08/28/2023

Maestro: Uncovering Low-Rank Structures via Trainable Decomposition

Deep Neural Networks (DNNs) have been a large driver and enabler for AI ...

0 Samuel Horvath, et al. ∙

∙ 05/30/2023

Clip21: Error Feedback for Gradient Clipping

Motivated by the increasing popularity and importance of large-scale tra...

0 Sarit Khirirat, et al. ∙

∙ 05/29/2023

Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees

Efficient distributed training is a principal driver of recent advances ...

0 Jihao Xin, et al. ∙

∙ 05/29/2023

Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity

We present a partially personalized formulation of Federated Learning (F...

0 Konstantin Mishchenko, et al. ∙

∙ 04/11/2023

Improving Performance of Private Federated Models in Medical Image Analysis

Federated learning (FL) is a distributed machine learning (ML) approach ...

0 Xiangjian Hou, et al. ∙

∙ 12/07/2022

Partial Disentanglement with Partially-Federated GANs (PaDPaF)

Federated learning has become a popular machine learning paradigm with m...

0 Abdulla Jasem Almansoori, et al. ∙

∙ 08/10/2022

Adaptive Learning Rates for Faster Stochastic Gradient Methods

In this work, we propose new adaptive step size strategies that improve ...

7 Samuel Horvath, et al. ∙

∙ 08/07/2022

Granger Causality using Neural Networks

The Granger Causality (GC) test is a famous statistical hypothesis test ...

8 Samuel Horvath, et al. ∙

∙ 07/01/2022

Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity

Federated learning (FL) is an emerging machine learning paradigm involvi...

0 Samuel Horvath, et al. ∙

∙ 06/01/2022

Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top

Byzantine-robustness has been gaining a lot of attention due to the grow...

0 Eduard Gorbunov, et al. ∙

∙ 04/27/2022

FedShuffle: Recipes for Better Use of Local Work in Federated Learning

The practice of applying several local updates before aggregation across...

1 Samuel Horvath, et al. ∙

∙ 02/07/2022

FL_PyTorch: optimization research simulator for federated learning

Federated Learning (FL) has emerged as a promising technique for edge de...

80 Konstantin Burlachenko, et al. ∙

∙ 11/22/2021

FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning

Federated Learning (FL) is an increasingly popular machine learning para...

14 Elnur Gasanov, et al. ∙

∙ 02/26/2021

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

Federated Learning (FL) has been gaining significant traction across dif...

0 Samuel Horvath, et al. ∙

∙ 02/25/2021

Hyperparameter Transfer Learning with Adaptive Complexity

Bayesian optimization (BO) is a sample efficient approach to automatical...

18 Samuel Horvath, et al. ∙

∙ 10/26/2020

Optimal Client Sampling for Federated Learning

It is well understood that client-master communication can be a primary ...

6 Wenlin Chen, et al. ∙

∙ 10/05/2020

Lower Bounds and Optimal Algorithms for Personalized Federated Learning

In this work, we consider the optimization formulation of personalized f...

0 Filip Hanzely, et al. ∙

∙ 06/19/2020

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

Modern large-scale machine learning applications require stochastic opti...

6 Samuel Horvath, et al. ∙

∙ 02/27/2020

On Biased Compression for Distributed Learning

In the last few years, various communication compression techniques have...

15 Aleksandr Beznosikov, et al. ∙

∙ 02/13/2020

Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization

Adaptivity is an important yet under-studied property in modern optimiza...

4 Samuel Horvath, et al. ∙

∙ 05/27/2019

Natural Compression for Distributed Deep Learning

Due to their hunger for big data, modern deep learning models are traine...

0 Samuel Horvath, et al. ∙

∙ 01/24/2019

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...

0 Dmitry Kovalev, et al. ∙

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