most citedA Relaxation Approach to Feature Selection for Linear Mixed Effects Models

2 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022★ 1 cited

Physics-Informed Koopman Network

Yuying Liu, Aleksei Sholokhov, Hassan Mansour +1

Koopman operator theory is receiving increased attention due to its promise to linearize nonlinear dynamics. Neural networks that are developed to represent Koopman operators have…

stat.ME2022★ 2 cited

Analysis of Relaxation Methods for Feature Selection in Mixed Effects Models

Aleksandr Aravkin, James Burke, Aleksei Sholokhov +1

Linear Mixed-Effects (LME) models are a fundamental tool for modeling clustered data, including cohort studies, longitudinal data analysis, and meta-analysis. The design and analys…

stat.ME2022★ 2 cited

A Relaxation Approach to Feature Selection for Linear Mixed Effects Models

Aleksei Sholokhov, James V. Burke, Damian F. Santomauro +2

Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis…

cs.IR2018

Towards Large-Scale Exploratory Search over Heterogeneous Sources

Mariia Seleznova, Anton Belyy, Aleksei Sholokhov

Since time immemorial, people have been looking for ways to organize scientific knowledge into some systems to facilitate search and discovery of new ideas. The problem was partial…

stat.ML2018

MEMOIR: Multi-class Extreme Classification with Inexact Margin

Anton Belyy, Aleksei Sholokhov

Multi-class classification with a very large number of classes, or extreme classification, is a challenging problem from both statistical and computational perspectives. Most of th…