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Slavomír Hanzely
Slavomír Hanzely
Postdoctoral Researcher, MBZUAI
Verified email at mbzuai.ac.ae - Homepage
Title
Cited by
Cited by
Year
Lower bounds and optimal algorithms for personalized federated learning
F Hanzely, S Hanzely, S Horváth, P Richtárik
Advances in Neural Information Processing Systems 33, 2304-2315, 2020
1932020
ZeroSARAH: Efficient nonconvex finite-sum optimization with zero full gradient computation
Z Li, S Hanzely, P Richtárik
arXiv preprint arXiv:2103.01447, 2021
302021
A Damped Newton Method Achieves Global and Local Quadratic Convergence Rate
S Hanzely, D Kamzolov, D Pasechnyuk, A Gasnikov, P Richtarik, M Takac
Advances in Neural Information Processing Systems 35, 25320-25334, 2022
192022
Distributed Newton-type methods with communication compression and Bernoulli aggregation
R Islamov, X Qian, S Hanzely, M Safaryan, P Richtárik
arXiv preprint arXiv:2206.03588, 2022
112022
Adaptive learning of the optimal mini-batch size of SGD
M Alfarra, S Hanzely, A Albasyoni, B Ghanem, P Richtárik
Workshop on Optimization for Machine Learning, NeurIPS 2020, 2020
10*2020
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes
K Mishchenko, S Hanzely, P Richtárik
arXiv preprint arXiv:2301.06806, 2023
42023
Adaptive Optimization Algorithms for Machine Learning
S Hanzely
arXiv preprint arXiv:2311.10203, 2023
32023
Sketch-and-Project Meets Newton Method: Global Convergence with Low-Rank Updates
S Hanzely
arXiv preprint arXiv:2305.13082, 2023
3*2023
DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning
K Ziu, S Hanzely, L Li, K Zhang, M Takáč, D Kamzolov
arXiv preprint arXiv:2410.23862, 2024
2024
Damped Newton Method with Near-Optimal Global Convergence Rate
S Hanzely, F Abdukhakimov, M Takáč
arXiv preprint arXiv:2405.18926, 2024
2024
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