Clustering with Prototype Extraction for Census Data Analysis - Oleg Chertov; Marharyta Aleksandrova (CoRR, 2011)

Fuzzy Clustering with Prototype Extraction for Census Data Analysis. - Oleg Chertov; Marharyta Aleksandrova (Soft Computing: State of the Art Theory and Novel Applications, 2013)

What about Interpreting Features in Matrix Factorization-based Recommender Systems as Users? - Marharyta Aleksandrova; Armelle Brun; Anne Boyer; Oleg Chertov (HT (Doctoral Consortium / Late-breaking Results / Workshops), 2014)

Can Latent Features Be Interpreted as Users in Matrix Factorization-Based Recommender Systems? - Armelle Brun; Marharyta Aleksandrova; Anne Boyer (WI-IAT (1), 2014)

Sets of Contrasting Rules to Identify Trigger Factors. - Marharyta Aleksandrova; Armelle Brun; Oleg Chertov; Anne Boyer (ECAI, 2016)

Sets of Contrasting Rules: A Supervised Descriptive Rule Induction Pattern for Identification of Trigger Factors. - Marharyta Aleksandrova; Armelle Brun; Oleg Chertov; Anne Boyer (ICTAI, 2016)

Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application. - Pavlo Tkachenko; Galyna Kriukova; Marharyta Aleksandrova; Oleg Chertov; Eric Renard; Sergei V. Pereverzyev (Comput. Methods Programs Biomed., 2016)

Contrast classification rules for mining local differences in medical data. - Marharyta Aleksandrova; Oleg Chertov; Armelle Brun; Anne Boyer (IDAACS, 2017)

Identifying representative users in matrix factorization-based recommender systems: application to solving the content-less new item cold-start problem. - Marharyta Aleksandrova; Armelle Brun; Anne Boyer; Oleg Chertov (J. Intell. Inf. Syst., 2017)

Matrix Factorization and Contrast Analysis Techniques for Recommendation. (Factorisation de matrices et analyse de contraste pour la recommandation). - Marharyta Aleksandrova (2017)

SCR-Apriori for Mining 'Sets of Contrasting Rules'. - Marharyta Aleksandrova; Oleg Chertov (CoRR, 2019)

BacAnalytics: A Tool to Support Secondary School Examination in France. - Azim Roussanaly; Marharyta Aleksandrova; Anne Boyer (ISMIS (Industrial Paper), 2020)

Security and Performance Implications of BGP Rerouting-Resistant Guard Selection Algorithms for Tor. - Asya Mitseva; Marharyta Aleksandrova; Thomas Engel 001; Andriy Panchenko 001 (SEC, 2020)

The Effect of Noise Level on the Accuracy of Causal Discovery Methods with Additive Noise Models. - Benjamin Kap; Marharyta Aleksandrova; Thomas Engel 001 (BNAIC/BENELEARN, 2021)

Impact of model-agnostic nonconformity functions on efficiency of conformal classifiers: an extensive study. - Marharyta Aleksandrova; Oleg Chertov (COPA, 2021)

k-Pareto optimality for many-objective genetic optimization. - Jean Ruppert; Marharyta Aleksandrova; Thomas Engel 001 (GECCO Companion, 2021)

How Nonconformity Functions and Difficulty of Datasets Impact the Efficiency of Conformal Classifiers. - Marharyta Aleksandrova; Oleg Chertov (CoRR, 2021)

Causal Identification with Additive Noise Models: Quantifying the Effect of Noise. - Benjamin Kap; Marharyta Aleksandrova; Thomas Engel 001 (CoRR, 2021)

k-Pareto Optimality-Based Sorting with Maximization of Choice. - Jean Ruppert; Marharyta Aleksandrova; Thomas Engel 001 (AISTATS, 2022)

k-Pareto Optimality-Based Sorting with Maximization of Choice and Its Application to Genetic Optimization. - Jean Ruppert; Marharyta Aleksandrova; Thomas Engel 001 (Algorithms, 2022)

Security and performance implications of BGP rerouting-resistant guard selection algorithms for Tor. - Asya Mitseva; Marharyta Aleksandrova; Andriy Panchenko 001 (Comput. Secur., 2023)