1 citations · 2 across the 10 of their papers we have counts for
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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…
CaCuTe: Casual Cubic-Model Technique for Faster Optimization
Nazarii Tupitsa
We establish a local rate for the gradient update under a -Hessian--Lipschitz assumption. Regime det…
On Solving Minimization and Min-Max Problems by First-Order Methods with Relative Error in Gradients
Artem Vasin, Valery Krivchenko, Dmitry Kovalev +6
First-order methods for minimization and saddle point (min-max) problems are widely used for solving large-scale problems, in particular arising in machine learning. The majority o…
Methods for Convex -Smooth Optimization: Clipping, Acceleration, and Adaptivity
Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury +4
Due to the non-smoothness of optimization problems in Machine Learning, generalized smoothness assumptions have been gaining a lot of attention in recent years. One of the most pop…
Primal-Dual Gradient Methods for Searching Network Equilibria in Combined Models with Nested Choice Structure and Capacity Constraints
Meruza Kubentayeva, Demyan Yarmoshik, Mikhail Persiianov +8
We consider a network equilibrium model (i.e. a combined model), which was proposed as an alternative to the classic four-step approach for travel forecasting in transportation net…
Numerical Methods for Large-Scale Optimal Transport
Nazarii Tupitsa, Pavel Dvurechensky, Darina Dvinskikh +1
The optimal transport (OT) problem is a classical optimization problem having the form of linear programming. Machine learning applications put forward new computational challenges…