1 citations · 1 across the 5 of their papers we have counts for
5 papers
Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics
Igor Ignashin, Anna Radovskaya, Andrew Semenov +7
Stochastic Gradient Descent (SGD) is commonly modeled as a Langevin process, assuming that minibatch noise acts as Brownian motion. However, this approximation relies on a continuo…
Stochastic Origin Frank-Wolfe for traffic assignment
Igor Ignashin, Demyan Yarmoshik, Andrei Raigorodskii
In this paper, we present the Stochastic Origin Frank-Wolfe (SOFW) method, which is a special case of the block-coordinate Frank-Wolfe algorithm, applied to the problem of finding…
Aligning Distributionally Robust Optimization with Practical Deep Learning Needs
Dmitrii Feoktistov, Igor Ignashin, Andrey Veprikov +4
While traditional Deep Learning (DL) optimization methods treat all training samples equally, Distributionally Robust Optimization (DRO) adaptively assigns importance weights to di…
Modeling skiers flows via Wardrope equilibrium in closed capacitated networks
Demyan Yarmoshik, Igor Ignashin, Ekaterina Sikacheva +1
We propose an equilibrium model of ski resorts where users are assigned to cycles in a closed network. As queues form on lifts with limited capacity, we derive an efficient way to…
Modifications of the Frank-Wolfe algorithm in the problem of finding an equilibrium distribution of traffic flows
Igor N. Ignashin, Demyan V. Yarmoshik
The paper presents various modifications of the Frank-Wolfe algorithm in the equilibrium traffic assignment problem. The Beckman model is used as a model for experiments. In this a…