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Slavomír Hanzely
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2020 – today
- 2024
- [i7]Klea Ziu, Slavomír Hanzely, Loka Li, Kun Zhang, Martin Takác, Dmitry Kamzolov:
ψDAG: Projected Stochastic Approximation Iteration for DAG Structure Learning. CoRR abs/2410.23862 (2024) - 2023
- [b1]Slavomír Hanzely:
Adaptive Optimization Algorithms for Machine Learning. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2023 - [j1]Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik:
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. Trans. Mach. Learn. Res. 2023 (2023) - [i6]Konstantin Mishchenko, Slavomír Hanzely, Peter Richtárik:
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes. CoRR abs/2301.06806 (2023) - [i5]Slavomír Hanzely:
Sketch-and-Project Meets Newton Method: Global O(k-2) Convergence with Low-Rank Updates. CoRR abs/2305.13082 (2023) - [i4]Slavomír Hanzely:
Adaptive Optimization Algorithms for Machine Learning. CoRR abs/2311.10203 (2023) - 2022
- [c2]Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander V. Gasnikov, Peter Richtárik, Martin Takác:
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate. NeurIPS 2022 - [i3]Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik:
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. CoRR abs/2206.03588 (2022) - 2020
- [c1]Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik:
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. NeurIPS 2020 - [i2]Motasem Alfarra, Slavomír Hanzely, Alyazeed Albasyoni, Bernard Ghanem
, Peter Richtárik
Adaptive Learning of the Optimal Mini-Batch Size of SGD. CoRR abs/2005.01097 (2020) - [i1]Filip Hanzely
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. CoRR abs/2010.02372 (2020)
Coauthor Index
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