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

Teaching (Winter 2021/2020)

RuleML Webinars

I organize monthly RuleML Webinars. (https://wiki.ruleml.org/index.php/RuleML_Webinar).

Upcoming webinars

2022-05-25. Ian Horrocks (Oxford University). Knowledge Graphs: Theory, Applications and Challenges.

2022-04-27. František Malinka (CTU). Semantic Biclustering with Rules.

News

April, 2022. Our paper resulting from cooperation with Dr. Joachimiak from Lawrence Berkeley National Laboratory is on-line first in the journal of Scientometrics:
- Beranová, Lucie, et al. "Why was this cited? Explainable machine learning applied to COVID-19 research literature." Scientometrics (2022): 1-37. https://t.co/l4wevWho0b
August, 2021. I will present our work on review cognitive biases in machine learning at the International Joint Conference on Artificial Intelligence (IJCAI-21)
August, 2021. I will serve as a program committee member of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI'22)
17 June 2021. Our paper on rule mining from graphs appears in an issue of the Semantic Web Journal.

Václav Zeman, Tomáš Kliegr and Vojtěch Svátek. RDFRules: Making RDF Rule Mining Easier and Even More Efficient. Semantic Web, vol. 12, no. 4, pp. 569-602, 2021 http://www.semantic-web-journal.net/system/files/swj2398.pdf

I serve on the program committee of RuleML+RR 2021 (DeclarativeAI 2021).
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.
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.