Samuel Horvath

Cited by

	All	Since 2017
Citations	510	505
h-index	10	10
i10-index	10	10

260

130

195

201920202021202231 103 252 114

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 MishchenkoCNRS & École Normale Supérieure & InriaVerified email at inria.fr
Sebastian Urban StichCISPA Helmholtz Center for Information SecurityVerified email at cispa.de
Mher SafaryanPostdoctoral fellow, King Abdullah University of Science & Technology (KAUST)Verified email at kaust.edu.sa
Aleksandr BeznosikovMSc student, Moscow Institute of Physics and TechnologyVerified email at phystech.edu
Slavomír HanzelyKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Wenlin ChenUniversity of Cambridge, Max Planck Institute for Intelligent SystemsVerified email at cam.ac.uk
Ilias LeontiadisResearch Scientist, FacebookVerified email at cl.cam.ac.uk
Stefanos LaskaridisResearcher @ Samsung AI Center CambridgeVerified email at samsung.com
Mário AlmeidaSamsung AI CenterVerified email at samsung.com
Nicholas LaneUniversity of Cambridge and Samsung AIVerified email at cam.ac.uk
Stylianos I. VenierisSamsung AI Center, Cambridge, UKVerified email at samsung.com
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
Cédric ArchambeauAmazon Web Services, BerlinVerified email at amazon.com
Aaron KleinAWS Research BerlinVerified email at cs.uni-freiburg.de

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	100	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	89	2019
Natural compression for distributed deep learning S Horváth, CY Ho, L Horvath, AN Sahu, M Canini, P Richtárik arXiv preprint arXiv:1905.10988, 2019	67	2019
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	64	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	54	2020
A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021	34	2021
Optimal Client Sampling for Federated Learning W Chen, S Horvath, P Richtarik NeurIPS 2020 workshop on Privacy Preserving Machine Learning, 2020	31	2020
Nonconvex variance reduced optimization with arbitrary sampling S Horváth, P Richtárik ICML 2019 - Proceedings of the 36th International Conference on Machine Learning, 2018	25	2018
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning S Horváth, P Richtárik ICLR 2021 - International Conference on Learning Representations, 2020	18	2020
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout S Horvath, S Laskaridis, M Almeida, I Leontiadis, SI Venieris, ND Lane 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021	16	2021
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization S Horváth, L Lei, P Richtárik, MI Jordan SIAM Journal on Mathematics of Data Science (SIMODS), 2022	7	2022
Hyperparameter transfer learning with adaptive complexity S Horváth, A Klein, P Richtárik, C Archambeau International Conference on Artificial Intelligence and Statistics, 1378-1386, 2021	4	2021
Long-term outcome in patients with takotsubo syndrome E Pogran, A El-Razek, L Gargiulo, V Weihs, C Kaufmann, S Horváth, ... Wiener klinische Wochenschrift 134 (7), 261-268, 2022	1	2022
FedShuffle: Recipes for Better Use of Local Work in Federated Learning S Horváth, M Sanjabi, L Xiao, P Richtárik, M Rabbat arXiv preprint arXiv:2204.13169, 2022		2022
FL_PyTorch: optimization research simulator for federated learning K Burlachenko, S Horváth, P Richtárik Proceedings of the 2nd ACM International Workshop on Distributed Machine …, 2021		2021
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning E Gasanov, A Khaled, S Horváth, P Richtárik AISTATS 2022, International Conference on Artificial Intelligence and Statistics, 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

The system can't perform the operation now. Try again later.

Articles 1–18

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow this author

Cited by

Co-authors