10 citations · 22 across the 13 of their papers we have counts for
24 papers · 1 filter
Accelerated and Stable Convergence with Anchored Generalized Optimistic Method
Motahareh Sohrabi, Jianxin You, Simon Lacoste-Julien +2
We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the extragradient method rely on two gradie…
Heterogeneous-Horizon Exact-Weight Local SGD
Dmitry Pasechnyuk-Vilensky, Martin Takáč
We study adaptive aggregation for heterogeneous local SGD in convex finite-sum optimization, allowing heterogeneous local horizons, minibatch sizes, gradient noise, and participati…
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
Artem Agafonov, Petr Ostroukhov, Roman Mozhaev +5
Variational inequalities represent a broad class of problems, including minimization and min-max problems, commonly found in machine learning. Existing second-order and high-order…
Median Clipping for Zeroth-order Non-Smooth Convex Optimization and Multi-Armed Bandit Problem with Heavy-tailed Symmetric Noise
Nikita Kornilov, Yuriy Dorn, Aleksandr Lobanov +5
In this paper, we consider non-smooth convex optimization with a zeroth-order oracle corrupted by symmetric stochastic noise. Unlike the existing high-probability results requiring…
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
Nikita Puchkin, Eduard Gorbunov, Nikolay Kutuzov +1
We consider stochastic optimization problems with heavy-tailed noise with structured density. For such problems, we show that it is possible to get faster rates of convergence than…
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
Nikita Kornilov, Ohad Shamir, Aleksandr Lobanov +5
In this paper, we consider non-smooth stochastic convex optimization with two function evaluations per round under infinite noise variance. In the classical setting when noise has…