| 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 | 193 | 2020 |
| ZeroSARAH: Efficient nonconvex finite-sum optimization with zero full gradient computation Z Li, S Hanzely, P Richtárik arXiv preprint arXiv:2103.01447, 2021 | 30 | 2021 |
| 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 | 19 | 2022 |
| 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 | 11 | 2022 |
| 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 | 4 | 2023 |
| Adaptive Optimization Algorithms for Machine Learning S Hanzely arXiv preprint arXiv:2311.10203, 2023 | 3 | 2023 |
| 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 |
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
