2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Gradient-Free Methods for Stochastic Convex Optimization with Stochastic Functional Constraints
Vadim Abronin, Alexander Gasnikov, Darina Dvinskikh
We develop accelerated gradient-free methods for stochastic convex optimization with constraints defined by expectations. Our batched primal-dual sliding method uses two-point eval…
Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity
Alexandra Suvorikova, Igor Pavlov, Artem Vasin +4
Zeroth-order (black-box) optimization is applied when gradients are unavailable and objective evaluations rely on expensive simulations. In many such applications, the oracle fidel…
Stochastic Optimization and Data Science
Arutyun Avetisyan, Darina Dvinskikh, Alexander Gasnikov +3
This paper aims to motivate stochastic optimization problems from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood…
Lower and upper bounds of the convergence rate of gradient methods with composite noise in gradient
Artem Vasin, Alexander Gasnikov
We introduce a detailed analysis of the convergence of first-order methods with composite noise (sum of relative and absolute) in gradient for convex and smooth function minimizati…
Stronger constraints for smooth min-max games
Valery Krivchenko, Alexander Gasnikov, Dmitry Kovalev
Saddle point problems with smooth convex-concave objective functions are often used to model min-max problems arising in machine learning. First-order methods are the standard para…