Samuel Horvath
Samuel Horvath
PhD Student, KAUST
Verified email at kaust.edu.sa - Homepage
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
532019
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
532019
Natural compression for distributed deep learning
S Horvath, CY Ho, L Horvath, AN Sahu, M Canini, P Richtarik
arXiv preprint arXiv:1905.10988, 2019
342019
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
182020
Nonconvex variance reduced optimization with arbitrary sampling
S Horváth, P Richtárik
ICML 2019 - Proceedings of the 36th International Conference on Machine Learning, 2018
182018
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
82020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
S Horváth, L Lei, P Richtárik, MI Jordan
NeurIPS 2020 OPT ML workshop, 2020
52020
Optimal Client Sampling for Federated Learning
W Chen, S Horvath, P Richtarik
NeurIPS 2020 workshop on Privacy Preserving Machine Learning, 2020
32020
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
S Horváth, P Richtárik
ICLR 2021 - International Conference on Learning Representations, 2020
22020
Hyperparameter Transfer Learning with Adaptive Complexity
S Horváth, A Klein, P Richtárik, C Archambeau
International Conference on Artificial Intelligence and Statistics, 1378-1386, 2021
2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
S Horvath, S Laskaridis, M Almeida, I Leontiadis, SI Venieris, ND Lane
arXiv preprint arXiv:2102.13451, 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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