5 papers
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…
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…
About some works of Boris Polyak on convergence of gradient methods and their development
Seydamet Ablaev, Aleksandr Beznosikov, Alexander Gasnikov +4
The paper presents a review of the state-of-the-art of subgradient and accelerated methods of convex optimization, including in the presence of disturbances and access to various i…
Accelerated zero-order SGD under high-order smoothness and overparameterized regime
Georgii Bychkov, Darina Dvinskikh, Anastasia Antsiferova +2
We present a novel gradient-free algorithm to solve a convex stochastic optimization problem, such as those encountered in medicine, physics, and machine learning (e.g., adversaria…
Bregman Proximal Method for Efficient Communications under Similarity
Aleksandr Beznosikov, Darina Dvinskikh, Dmitry Bylinkin +2
We propose a novel stochastic distributed method for both monotone and strongly monotone variational inequalities with Lipschitz operator and proper convex regularizers arising in…