| Gradient methods for problems with inexact model of the objective FS Stonyakin, D Dvinskikh, P Dvurechensky, A Kroshnin, O Kuznetsova, ... Mathematical Optimization Theory and Operations Research: 18th International …, 2019 | 58 | 2019 |
| Inexact model: A framework for optimization and variational inequalities F Stonyakin, A Tyurin, A Gasnikov, P Dvurechensky, A Agafonov, ... Optimization Methods and Software 36 (6), 1155-1201, 2021 | 47 | 2021 |
| An accelerated second-order method for distributed stochastic optimization A Agafonov, P Dvurechensky, G Scutari, A Gasnikov, D Kamzolov, ... 2021 60th IEEE Conference on Decision and Control (CDC), 2407-2413, 2021 | 19 | 2021 |
| Inexact relative smoothness and strong convexity for optimization and variational inequalities by inexact model F Stonyakin, A Tyurin, A Gasnikov, P Dvurechensky, A Agafonov, ... arXiv preprint arXiv:2001.09013, 2020 | 19 | 2020 |
| Inexact tensor methods and their application to stochastic convex optimization A Agafonov, D Kamzolov, P Dvurechensky, A Gasnikov, M Takáč Optimization Methods and Software, 1-42, 2023 | 17 | 2023 |
| Inexact model: A framework for optimization and variational inequalities F Stonyakin, A Gasnikov, A Tyurin, D Pasechnyuk, A Agafonov, ... arXiv preprint arXiv:1902.00990, 2019 | 15 | 2019 |
| Flecs: A federated learning second-order framework via compression and sketching A Agafonov, D Kamzolov, R Tappenden, A Gasnikov, M Takáč arXiv preprint arXiv:2206.02009, 2022 | 11 | 2022 |
| Exploiting higher-order derivatives in convex optimization methods D Kamzolov, A Gasnikov, P Dvurechensky, A Agafonov, M Takáč arXiv preprint arXiv:2208.13190, 2022 | 5 | 2022 |
| Accelerated adaptive cubic regularized quasi-newton methods D Kamzolov, K Ziu, A Agafonov, M Takác arXiv preprint arXiv:2302.04987, 2, 2023 | 4 | 2023 |
| In Quest of Ground Truth: Learning Confident Models and Estimating Uncertainty in the Presence of Annotator Noise AA Hashmi | 3 | 2022 |
| Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness A Agafonov, D Kamzolov, A Gasnikov, K Antonakopoulos, V Cevher, ... arXiv preprint arXiv:2309.01570, 2023 | 2 | 2023 |
| Cubic Regularized Quasi-Newton Methods D Kamzolov, K Ziu, A Agafonov, M Takác arXiv preprint arXiv:2302.04987, 2023 | 2 | 2023 |
| FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences A Agafonov, B Erraji, M Takáč arXiv preprint arXiv:2210.09626, 2022 | 2 | 2022 |
| Cubic Regularization is the Key! The First Accelerated Quasi-Newton Method with a Global Convergence Rate of for Convex Functions D Kamzolov, K Ziu, A Agafonov, M Takáč arXiv preprint arXiv:2302.04987, 2023 | 1 | 2023 |
| Lower bounds for conditional gradient type methods for minimizing smooth strongly convex functions A Agafonov arXiv preprint arXiv:2003.07073, 2020 | 1 | 2020 |
| Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations A Agafonov, P Ostroukhov, K Yakovlev, E Gorbunov, M Takáč, A Gasnikov, ... arXiv preprint arXiv:2405.15990, 2024 | | 2024 |
| Нижние оценки для методов типа условного градиента для задач минимизации гладких сильно выпуклых функций АД Агафонов Компьютерные исследования и моделирование 14 (2), 213-223, 2022 | | 2022 |
| Градиентные методы для задач оптимизации, допускающие существование неточной сильно выпуклой модели целевой функции АД Агафонов, ФС Стонякин Труды Московского физико-технического института 11 (3 (43)), 4-19, 2019 | | 2019 |
Alexander GasnikovMIPT在 mipt.ru 的电子邮件经过验证
