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https://ceur-ws.org/Vol-3711/preface.pdf
Preface: modern machine learning technologies
workshop
Michael Emmerich1,2,3, ∗ ,†, Vasyl Lytvyn4,† and Victoria Vysotska4,5,†
1
Leiden Institute of Advanced Computer Science, LIACS Leiden University, Niels Bohrweg 1, 2333CA
Leiden, The Netherlands
2
Department of IT, University of Jyväskylä, Mattilanniemi 2, 40100 Jyväskylä, Finland
3
Lead AI Scientist @ SILO.ai, Lapinlahdenkatu 1 C, 00180 Helsinki, Finland
4
Lviv Polytechnic National University, Stepan Bandera 12, 79013 Lviv, Ukraine
5
Osnabrück University, Friedrich-Janssen-Str. 1, 49076 Osnabrück, Germany
Abstract
This document is the preface of the 6th International Workshop on Modern Machine Learning
Technologies (MoMLeT-2024), May, 31 - June, 1, 2024, held in Lviv-Shatsk, Ukraine. The
main purpose of the MoMLeT Workshop is providing a forum for researchers to discuss
models for machine learning, multicriteria decision analysis and multi-objective optimization,
and their real-life applications.
Keywords
machine learning, deep learning, model, method, theory, tools, technology, system,
application 1
1. Introduction
The main purpose of the Modern Machine Learning Technologies Workshop is providing
a forum for researchers to discuss models for machine learning, multicriteria decision
analysis and multi-objective optimization, and their real-life applications [1-5]. In MoMLeT
Workshop, we encourage the submission of papers on deep learning, decision making,
and multicriteria decision analysis areas. The MoMLeT Workshop is soliciting literature
review, survey and research papers comments including, whilst not limited to, the
following areas of interest:
Regression analysis;
Deep learning;
MoMLeT-2024: 6th International Workshop on Modern Machine Learning Technologies, May, 31 - June, 1,
2024, Lviv-Shatsk, Ukraine
∗
Corresponding author.
†
These authors contributed equally.
m.t.m.emmerich@liacs.leidenuniv.nl (M. Emmerich); Vasyl.V.Lytvyn@lpnu.ua (V. Lytvyn);
victoria.a.vysotska@lpnu.ua (V. Vysotska)
0000-0002-7342-2090 (M. Emmerich); 0000-0002-9676-0180 (V. Lytvyn); 0000-0001-6417-3689 (V.
Vysotska);
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Gradient Boosted Trees;
Support Vector Machines;
Bayesian networks;
Unsupervised learning for clustering;
MCDM Theory;
Multi-objective Optimization;
Group Decision Making;
Multi-attribute Utility or Value Theory;
Behavioral Issues in Decision Making;
Preference Modelling;
Applications of MCDM and Optimization.
The language of Modern Machine Learning Technologies Workshop is English.
The Modern Machine Learning Technologies Workshop took the form of oral
presentation by peer-reviewed individual papers. The papers were distributed among 32
external reviewers from The Netherlands, Finland, Germany, France, United Kingdom,
China, Austria, Czech Republic, Portugal, India, Poland, Ukraine and Ukraine.
The Modern Machine Learning Technologies Workshop gathered participants from
different countries including Germany, USA, United Kingdom, The Netherlands, Finland,
Poland, China, and Ukraine.
This year Organizing Committee received 39 submissions, out of which 21 were
accepted for presentation as a regular paper. These papers and extended abstracts
were published in this Volume I of the 6th International Workshop on Modern Machine
Learning Technologies (MoMLeT 2024) proceedings.
2. Acknowledgments
The Modern Machine Learning Technologies Workshop would not have been
possible without the support of many people. First of all, we would like to thank all the
authors who submitted papers to Modern Machine Learning Technologies Workshop
and thus demonstrated their interest in the research problems within our scope. We are
very grateful to the members of our Program Committee for providing timely and
thorough reviews and, also, for being cooperative in doing additional review work. We
would like to thank the Organizing Committee of the workshop whose devotion and
efficiency made this instance of Modern Machine Learning Technologies Workshop a
very interesting and effective scientific forum. We would like to thank Modern Machine
Learning Technologies Workshop Chairs, as well as Program Committee and all
Reviewers, for their diligence in selecting the papers and ensuring their high scientific
quality.
References
[1] M. Emmerich, V. Vysotska, V. Lytvynenko, Proceedings of the Modern Machine
Learning Technologies and Data Science Workshop (MoMLeT&DS 2023), Lviv,
Ukraine, June 3, 2023, CEUR Workshop Proceedings 3426, CEUR-WS.org 2023.
URL: https://dblp.uni-trier.de/db/conf/momlet/momlet2023.html, https://ceur-
ws.org/Vol-3426/.
[2] M. Emmerich, V. Vysotska, Proceedings of the Modern Machine Learning
Technologies and Data Science Workshop (MoMLeT&DS 2022), Leiden-Lviv, The
Netherlands-Ukraine, November 25-26, 2022, CEUR Workshop Proceedings 3312,
CEUR-WS.org 2023. URL: https://dblp.uni-
trier.de/db/conf/momlet/momlet2022.html, https://ceur-ws.org/Vol-3312/.
[3] M. Emmerich, V. Lytvyn, V. Vysotska, V. Lytvynenko, V. Basto-Fernandes,
Proceedings of the Modern Machine Learning Technologies and Data Science
Workshop (MoMLeT&DS 2021), Lviv-Shatsk, Ukraine, June 5-6, 2021, CEUR
Workshop Proceedings 2917, CEUR-WS.org 2021. URL: https://dblp.uni-
trier.de/db/conf/momlet/momlet2021.html, https://ceur-ws.org/Vol-2917/.
[4] M. Emmerich, V. Lytvyn, V. Vysotska, V. Basto-Fernandes, V. Lytvynenko,
Proceedings of the Modern Machine Learning Technologies and Data Science
Workshop (MoMLeT&DS 2020), Lviv-Shatsk, Ukraine, June 2-3, 2020, CEUR
Workshop Proceedings 2631, CEUR-WS.org 2020. URL: https://dblp.uni-
trier.de/db/conf/momlet/momlet2020.html, https://ceur-ws.org/Vol-2631/.
[5] M. Emmerich, V. Lytvyn, I. Yevseyeva, V. Basto-Fernandes, D. Dosyn, V. Basto-
Fernandes, V. Vysotska, Proceedings of the Modern Machine Learning
Technologies and Data Science Workshop (MoMLeT&DS 2019), Lviv-Shatsk,
Ukraine, June 2-4, 2019, CEUR Workshop Proceedings 2386, CEUR-WS.org 2019.
URL: https://dblp.uni-trier.de/db/conf/momlet/momlet2019.html, https://ceur-
ws.org/Vol-2386/.