Samuel Horvath

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	All	Since 2016
Citations	174	173
h-index	5	5
i10-index	5	5

120

20192020202132 113 28

Co-authors

Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
Dmitry KovalevKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Marco CaniniKAUSTVerified email at kaust.edu.sa
Chen-Yu HoKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Atal N. SahuMS-PhD student, KAUSTVerified email at kaust.edu.sa
Konstantin MishchenkoKAUSTVerified email at kaust.edu.sa
Sebastian Urban StichResearch Scientist, EPFLVerified email at epfl.ch
Mher SafaryanKAUSTVerified email at kaust.edu.sa
Aleksandr BeznosikovMoscow Institute of Physics and TechnologyVerified email at phystech.edu
Filip HanzelyResearch Assistant Professor, TTICVerified email at ttic.edu
LIHUA LEIPostdoctoral researcher in statistics, Stanford UniversityVerified email at stanford.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Slavomír HanzelyKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Wenlin ChenUniversity of CambridgeVerified email at cam.ac.uk
Sélim ChraibiPhD student, Grenoble Alpes UniversitéVerified email at univ-grenoble-alpes.fr
Adil SalimKAUSTVerified email at kaust.edu.sa

Samuel Horvath

PhD Student, KAUST

Verified email at kaust.edu.sa - Homepage

Machine Learning Distributed Optimization Federated Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Stochastic Distributed Learning with Gradient Quantization and Variance Reduction S Horvath, D Kovalev, K Mishchenko, SU Stich, P Richtarik arXiv preprint arXiv:1904.05115, 2019	50	2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop D Kovalev, S Horváth, P Richtárik ALT 2020 - Proceedings of the 31st International Conference on Algorithmic …, 2019	48	2019
Natural compression for distributed deep learning S Horvath, CY Ho, L Horvath, AN Sahu, M Canini, P Richtarik arXiv preprint arXiv:1905.10988, 2019	31	2019
Nonconvex variance reduced optimization with arbitrary sampling S Horváth, P Richtárik ICML 2019 - Proceedings of the 36th International Conference on Machine Learning, 2018	18	2018
On Biased Compression for Distributed Learning A Beznosikov, S Horváth, P Richtárik, M Safaryan NeurIPS 2020, Workshop on Scalability, Privacy, and Security in Federated …, 2020	17	2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization S Horváth, L Lei, P Richtárik, MI Jordan NeurIPS 2020 OPT ML workshop, 2020	4	2020
Lower bounds and optimal algorithms for personalized federated learning F Hanzely, S Hanzely, S Horváth, P Richtárik 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020	3	2020
Optimal Client Sampling for Federated Learning W Chen, S Horvath, P Richtarik NeurIPS 2020 workshop on Privacy Preserving Machine Learning, 2020	2	2020
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning S Horváth, P Richtárik ICLR 2021 - International Conference on Learning Representations, 2020	1	2020
Hyperparameter Transfer Learning with Adaptive Complexity S Horváth, A Klein, P Richtárik, C Archambeau arXiv preprint arXiv:2102.12810, 2021		2021
Learning to Optimize via Dual space Preconditioning S Chraibi, A Salim, S Horváth, F Hanzely, P Richtárik		2019
IntML: Natural Compression for Distributed Deep Learning S Horváth, CY Ho, L Horváth, AN Sahu, M Canini, P Richtárik Workshop on AI Systems at Symposium on Operating Systems Principles 2019 …, 2019		2019

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