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Tomáš Kliegr, Ph.D. is an associate professor in the Faculty of Informatics and Statistics of University of Economics Prague, where he works in the Data Mining and Knowledge Discovery group.

University homepage | Office hours | Bio |Selected publications | ResearchGate | Google Scholar profile | email: tomas(dot)kliegr(at)vse(dot)cz

Teaching (Winter 2020/2021)

• Information and knowledge processing

• Data Science in Python and R

RuleML Webinars

2021-02-24 Marek Dudas (ContextMinds.com). Supporting argumentation with graph visualizations - a legal use case.

2020-11-25 Davide Sottara (Mayo Clinic). From SME to Screen - A Knowledge Architecture for Clinical Cognitive Support

(https://wiki.ruleml.org/index.php/RuleML_Webinar).

News

• February 17 2021. I was invited to present at the First international school on teaching and learning Machine Learning in Business Schools.

• February 10, 2021. The Artificial Intelligence journal published the on-line first version of our article: Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz. "A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models". The article is open access.

• Submission deadline: March 31, 2021. You are invited to submit to the Special Issue on Explainable and Interpretable Machine Learning and Data Mining of the Data Mining and Knowledge Discovery journal (DMKD, Springer). Guest editors: Martin Atzmueller, Johannes Fürnkranz (editor-in-chief), Tomáš Kliegr, Ute Schmid.

• 15 October 2020. Our paper on rule mining from graphs was accepted in the Semantic Web Journal.

Václav Zeman, Tomáš Kliegr and Vojtěch Svátek. RDFRules: Making RDF Rule Mining Easier and Even More Efficient http://www.semantic-web-journal.net/system/files/swj2398.pdf

• 8 September 2020. Proceedings or RuleML+RR are now on-line. https://www.springer.com/978-3-030-57976-0

• 21 August 2020. Our paper Editable Machine Learning Models? A rule-based framework for user studies of explainability will soon appear in Journal of Advances in Data Analysis and Classification (Springer) Preprint: https://nb.vse.cz/~klit01/papers/RuleEditor.pdf Update: paper is now on-line.

• Paper presenting my student's master thesis Action Rules: Counterfactual Explanations in Python is the winner of the 14th Rule Challenge 2020 competition. Paper is freely available at http://ceur-ws.org/Vol-2644/. Lukáš joins our department as a PhD student.

• 1 February 2020. Our paper Advances in machine learning for the behavioral sciences appears in a printed issue of American Behavioral Scientist. Preprint: https://arxiv.org/abs/1911.03249

• 27 Dec 2019. Paper "Associative Classification in R: arc, arulesCBA, and rCBA" featuring my arc package was published in the R Journal. This is a joint paper with Michael Hahsler (Southern Metodist University) and Ian Johnson (Google), authors of arulesCBA, and Jaroslav Kuchař (Czech Technical University), author of rCBA.

• 24 Dec 2019. Our paper "On Cognitive Preferences and the Interpretability of Rule-based Models." is on-line first in Machine Learning (Springer) - Open Access.

• I serve as program co-chair of RuleML+RR 2020@DeclarativeAI conference in Oslo.

• 22 Sep 2019. Our paper "Tuning Hyperparameters of Classification Based on Associations (CBA)" was presented at ITAT 2019 in Donovaly

• 19 Sep 2019 Our paper PyIDS–Python Implementation of Interpretable Decision Sets Algorithm by Lakkaraju et al, 2016. received the best RuleML Challenge Award at RuleML+RR 2019 in Bolzano.