activity
20182025
most citedAutomated Evolutionary Approach for the Design of Composite Machine Learning Pipelines

80 citations · 154 across the 10 of their papers we have counts for

collaborators

15 papers

cs.AI2025★ 1 cited

MADD: Multi-Agent Drug Discovery Orchestra

Gleb V. Solovev, Alina B. Zhidkovskaya, Anastasia Orlova +18

Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particula…

cs.LG2023

Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines

Nikolay O. Nikitin, Maiia Pinchuk, Valerii Pokrovskii +7

Automated machine learning (AutoML) systems propose an end-to-end solution to a given machine learning problem, creating either fixed or flexible pipelines. Fixed pipelines are tas…

cs.NE2022★ 9 cited

Generative Design of Physical Objects using Modular Framework

Nikita O. Starodubcev, Nikolay O. Nikitin, Konstantin G. Gavaza +3

In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are demonstrating intensive growth…

cs.NE2022★ 4 cited

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Nikita O. Starodubcev, Nikolay O. Nikitin, Anna V. Kalyuzhnaya

In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computa…

cs.LG2022★ 2 cited

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering

Petr Andriushchenko, Irina Deeva, Anna Bubnova +4

The work focuses on the modelling and imputation of oil and gas reservoirs parameters, specifically, the problem of predicting the oil recovery factor (RF) using Bayesian networks…

cs.LG2021★ 80 cited

Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines

Nikolay O. Nikitin, Pavel Vychuzhanin, Mikhail Sarafanov +6

The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the desi…