6 citations · 11 across the 7 of their papers we have counts for
7 papers
Accelerated Stochastic Gradient Method with Applications to Consensus Problem in Markov-Varying Networks
Vladimir Solodkin, Savelii Chezhegov, Ruslan Nazikov +2
Stochastic optimization is a vital field in the realm of mathematical optimization, finding applications in diverse areas ranging from operations research to machine learning. In t…
Accelerated Methods with Compression for Horizontal and Vertical Federated Learning
Sergey Stanko, Timur Karimullin, Aleksandr Beznosikov +1
Distributed optimization algorithms have emerged as a superior approaches for solving machine learning problems. To accommodate the diverse ways in which data can be stored across…
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
Andrei Semenov, Vladimir Ivanov, Aleksandr Beznosikov +1
We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBMs). While SOTA approaches to Image Classification task work as a black b…
Optimal Analysis of Method with Batching for Monotone Stochastic Finite-Sum Variational Inequalities
Alexander Pichugin, Maksim Pechin, Aleksandr Beznosikov +1
Variational inequalities are a universal optimization paradigm that is interesting in itself, but also incorporates classical minimization and saddle point problems. Modern realiti…
Optimal Data Splitting in Distributed Optimization for Machine Learning
Daniil Medyakov, Gleb Molodtsov, Aleksandr Beznosikov +1
The distributed optimization problem has become increasingly relevant recently. It has a lot of advantages such as processing a large amount of data in less time compared to non-di…
Activations and Gradients Compression for Model-Parallel Training
Mikhail Rudakov, Aleksandr Beznosikov, Yaroslav Kholodov +1
Large neural networks require enormous computational clusters of machines. Model-parallel training, when the model architecture is partitioned sequentially between workers, is a po…