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Tomáš Kliegr, Ph.D. is an associate professor in 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 | Selected publications | ResearchGate | Google Scholar profile | email: tomas(dot)kliegr(at)vse(dot)cz

Teaching (Summer 2019/2020)

• Information and knowledge processing
• Data Science in Python and R

Upcoming conferences

• International Workshop on Explainable and Interpretable Machine Learning (XI-ML) co-located with KI 2020, Sept. 21, 2020, Bamberg, Germany. Submission deadline: Jun 23, 2020

Upcoming RuleML Webinars

I run the RuleML webinar (https://wiki.ruleml.org/index.php/RuleML_Webinar)

29.4. 15:00 Johannes Rabold (Univ. Bamberg). Enriching Visual with Verbal Explanations for Relational Concepts – Combining LIME with Aleph

News

• 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.

Research interests

• explainable machine learning (rule learning, cognitively-inspired machine learning)
• natural language processing (entity recognition, knowledge graphs)
• preference learning, utility theory

Membership in Professional societies

DBpedia association, Czech society for cybernetics and Informatics (Artificial Intelligence working group), Gesellschaft für Klassifikation (Data Science Society)