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Artem Agafonov
Artem Agafonov
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Подтвержден адрес электронной почты в домене mbzuai.ac.ae
Название
Процитировано
Процитировано
Год
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
632019
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
572021
Inexact tensor methods and their application to stochastic convex optimization
A Agafonov, D Kamzolov, P Dvurechensky, A Gasnikov, M Takáč
Optimization Methods and Software 39 (1), 42-83, 2024
212024
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
202021
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
192020
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
162019
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
132022
Accelerated adaptive cubic regularized Quasi-Newton methods
D Kamzolov, K Ziu, A Agafonov, M Takác
arXiv preprint arXiv:2302.04987, 2, 2023
92023
Exploiting Higher Order Derivatives in Convex Optimization Methods
D Kamzolov, A Gasnikov, P Dvurechensky, A Agafonov, M Takáč
Encyclopedia of Optimization, 1-13, 2023
72023
Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness
A Agafonov, D Kamzolov, A Gasnikov, A Kavis, K Antonakopoulos, ...
arXiv preprint arXiv:2309.01570, 2023
62023
In quest of ground truth: Learning confident models and estimating uncertainty in the presence of annotator noise
AA Hashmi
42022
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
32023
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
32022
Optami: Global superlinear convergence of high-order methods
D Kamzolov, D Pasechnyuk, A Agafonov, A Gasnikov, M Takáč
arXiv preprint arXiv:2410.04083, 2024
12024
Lower bounds for conditional gradient type methods for minimizing smooth strongly convex functions
A Agafonov
arXiv preprint arXiv:2003.07073, 2020
12020
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
A Agafonov, P Ostroukhov, R Mozhaev, K Yakovlev, E Gorbunov, M Takác, ...
Advances in Neural Information Processing Systems 37, 115816-115860, 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
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Статьи 1–18