Lower Bounds and Optimal Algorithms for Personalized Federated Learning. - Filip Hanzely; Slavomír Hanzely; Samuel Horváth; Peter Richtárik (NeurIPS, 2020)

Adaptive Learning of the Optimal Mini-Batch Size of SGD. - Motasem Alfarra; Slavomír Hanzely; Alyazeed Albasyoni; Bernard Ghanem; Peter Richtárik (CoRR, 2020)

A Damped Newton Method Achieves Global $ mathcal O left( frac{1}{k^2} right)$ and Local Quadratic Convergence Rate. - Slavomír Hanzely; Dmitry Kamzolov; Dmitry Pasechnyuk; Alexander V. Gasnikov; Peter Richtárik; Martin Takác 001 (NeurIPS, 2022)

Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes. - Konstantin Mishchenko; Slavomír Hanzely; Peter Richtárik (CoRR, 2023)

Sketch-and-Project Meets Newton Method: Global O(k-2) Convergence with Low-Rank Updates. - Slavomír Hanzely (CoRR, 2023)

Adaptive Optimization Algorithms for Machine Learning. - Slavomír Hanzely (CoRR, 2023)

Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. - Rustem Islamov; Xun Qian; Slavomír Hanzely; Mher Safaryan; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

Adaptive Optimization Algorithms for Machine Learning. - Slavomír Hanzely (2023)

ψDAG: Projected Stochastic Approximation Iteration for DAG Structure Learning. - Klea Ziu; Slavomír Hanzely; Loka Li; Kun Zhang 001; Martin Takác 001; Dmitry Kamzolov (CoRR, 2024)

Sketch-and-Project Meets Newton Method: Global O(1/k2) Convergence with Low-Rank Updates. - Slavomír Hanzely (AISTATS, 2025)