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Konstantin Burlachenko
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MARINA: Faster non-convex distributed learning with compression
E Gorbunov, KP Burlachenko, Z Li, P Richtárik
International Conference on Machine Learning, 3788-3798, 2021
942021
Federated optimization algorithms with random reshuffling and gradient compression
A Sadiev, G Malinovsky, E Gorbunov, I Sokolov, A Khaled, K Burlachenko, ...
arXiv preprint arXiv:2206.07021, 2022
172022
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
162021
Faster rates for compressed federated learning with client-variance reduction
H Zhao, K Burlachenko, Z Li, P Richtárik
SIAM Journal on Mathematics of Data Science 6 (1), 154-175, 2024
122024
Federated learning with regularized client participation
G Malinovsky, S Horváth, K Burlachenko, P Richtárik
arXiv preprint arXiv:2302.03662, 2023
72023
Personalized federated learning with communication compression
EH Bergou, K Burlachenko, A Dutta, P Richtárik
arXiv preprint arXiv:2209.05148, 2022
42022
Sharper rates and flexible framework for nonconvex SGD with client and data sampling
A Tyurin, L Sun, K Burlachenko, P Richtárik
arXiv preprint arXiv:2206.02275, 2022
32022
Lane detection using Fourier based line detector
K Burlachenko
12013
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants
P Richtárik, E Gasanov, K Burlachenko
arXiv preprint arXiv:2402.10774, 2024
2024
Federated Learning is Better with Non-Homomorphic Encryption
K Burlachenko, A Alrowithi, FA Albalawi, P Richtarik
Proceedings of the 4th International Workshop on Distributed Machine …, 2023
2023
Error Feedback Shines when Features are Rare
P Richtárik, E Gasanov, K Burlachenko
arXiv preprint arXiv:2305.15264, 2023
2023
C++ from 1998 to 2020
K Burlachenko
https://github.com/burlachenkok/CPP_from_1998_to_2020, 2022
2022
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