Jilles Vreeken
Professor of Computer Science
Saarland University
Stuhlsatzenhaus 5, Room 1.08
66123 Saarbrücken, Germany
vreeken@cispa.de
+49 681 87083 2173
Jilles at work in San Francisco

I am faculty (W3, tenured) at the CISPA Helmholtz Center for Information Security, where I lead the research group on Exploratory Data Analysis. I'm Honorary Professor of Computer Science at Saarland University and ELLIS Faculty of the Saarbrücken Unit on Artificial Intelligence and Machine Learning.

My research is concerned with causality and unsupervised learning. In particular, I enjoy developing theory and algorithms for answering fundamentally exploratory questions, such as 'what is going on in my data?', 'what causes what and how?', 'what can we learn from this model?' without having to make unnecessary or unjustified assumptions. To identify what is worth knowing, I like to take a principled approach, such as based on information theory, and then proceed to develop efficient algorithms for extracting useful interpretable results. I like all data types equally much.

I am interested in causal inference under realistic conditions, such as e.g. under hidden confounding or selection bias, when the i.i.d. assumption does not hold, or while making use of background knowledge. I am always interested in how to summarize the essence of complex data and models in easily understandable and actionable terms, and using these to obtain better, more robust, and more useful models. I find the combination of these two threads especially exciting.


If you are looking for a bit of procrastination, consider
Research in Progress — the secret life of research, through the medium of animated GIFs.


Activities more ▾

Teaching and Advising more ▾
  • Postdoctoral Researchers
    • Dr. Janis Kalofolias
  • Doctoral Researchers
    • Corinna Coupette
    • Joscha Cueppers
    • Sebastian Dalleiger
    • David Kaltenpoth
    • Sarah Mameche
    • Osman Ali Mian
    • Nils Walter
    • Boris Wiegand
    • Sascha Xu
  • Research Assistants
    • Muneeb Aadil
    • Tim Bauerschmidt
    • Rafailia-Maria Chatzianastasiou
    • Felix Falkenberg
    • Ravil Gasanov
    • Nisha George
    • Paul Krieger
    • Marco Schuster
    • Aleena Siji
    • Jyotsna Singh
    • Kiet Vo
    • Anton Voran
    • Matthias Wilms
    • Mohammad Yaseen
  • Former MSc Thesis Students
    • Martin Gassner (2022)
    • Saif Ali Khan (2022)
    • Ekatarina Arkhangelskaya (2022)
    • Abraham Ezema (2021)
    • Tim Bruxmeier (2021)
    • Frauke Hinrichs (2021)
    • Sarah Mameche (2021)
    • Jana Hess (2021)
    • Anna Oláh (2020)
    • Edith Heiter (2020)
    • Sandra Sukarieh (2020)
    • Joscha Cueppers (2019)
    • Divyam Saran (2019)
    • Osman Ali Mian (2019)
    • Simina Ana Cotop (2019)
    • Magnus Halbe (2018)
    • Maha Aburahma (2018)
    • Iva Farag-Baykova (2018)
    • Yuliia Brendel (2018)
    • Maike Eissfeller (2018)
    • Boris Wiegand (2018)
    • Tatiana Dembelova (2018)
    • Robin Burghartz (2017)
    • Henrik Jilke (2017)
    • Benjamin Hättasch (2017)
    • Amirhossein Baradaranshahroudi (2016)
    • Apratim Bhattacharyya (2016)
    • Beata Wójciak (2016)
    • Margarita Salyaeva (2016)
    • Manan Gandhi (2016)
    • Kathrin Grosse (2016)
    • Kailash Budhathoki (2015)
    • Panagiotis Mandros (2015)
    • Thomas Van Brussel (2012)
    • Tanja Van den Eede (2011)
    • Sandy Moens (2010)
    • Andie Similon (2010)
    • Sander Schuckmann (2008)
  • Former BSc Thesis Students
    • Matthias Wilms (2021)
    • Daniel Kindler (2021)
    • Frauke Hinrichs (2017)
    • Magnus Halbe (2016)
    • Stefan Bier (2014)
  • Former Research Assistants
    • Khánh Hiep Tran
    • Grégoire Pacreau
    • Michael Hedderich
    • Patrick Ferber
    • Shweta Mahajan
    • Tobias Heinen
    • Cristian Caloian
    • David Ziegler
    • Stefan Neumann
    • Andrea Fuksova
    • Eustace Ebhotemhen
    • Shilpa Garg
    • Sinan Bozca
    • Michael Wessely

Selected Recent Publications (go here for the complete list)
2023
Wiegand, B, Klakow, D & Vreeken, J Why Are We Waiting? Discovering Interpretable Models to Predict Waiting and Sojourn Times from Data. In: SIAM International Conference on Data Mining (SDM), SIAM, 2023. (27.4% acceptance rate)
Mian, O, Kamp, M & Vreeken, J Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2023. (19.6% acceptance rate)
Kaltenpoth, D & Vreeken, J Identifying Selection Bias from Observational Data. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2023. (oral presentation, 10.8% acceptance rate; 19.6% overall)
2022
Dalleiger, S & Vreeken, J Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. In: Proceedings of Neural Information Processing Systems (NeurIPS), PMLR, 2022. (25.7% acceptance rate)project website
Mameche, S, Kaltenpoth, D & Vreeken, J Discovering Invariant and Changing Mechanisms from Data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 1242-1252, ACM, 2022. (15.0% acceptance rate)
Dalleiger, S & Vreeken, J Discovering Significant Patterns under Sequential False Discovery Control. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 263-272, ACM, 2022. (15.0% acceptance rate)project website
Hedderich, M, Fischer, J, Klakow, D & Vreeken, J Label-Descriptive Patterns and their Application to Characterizing Classification Errors. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2022. (21.9% acceptance rate)project website
Xu, S, Mian, O, Marx, A & Vreeken, J Inferring Cause and Effect in the Presence of Heteroscedastic Noise. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2022. (21.9% acceptance rate)project website
Wiegand, B, Klakow, D & Vreeken, J Mining Interpretable Data-to-Sequence Generators. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2022. (15.0% acceptance rate)project website
Coupette, C, Dalleiger, S & Vreeken, J Differentially Describing Groups of Graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2022. (oral presentation 5.5% acceptance rate; overall 15.0%)project website
Kalofolias, J & Vreeken, J Naming the most anomalous cluster in Hilbert Space for structures with attribute information. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2022. (15.0% acceptance rate)project website
Cueppers, J, Kalofolias, J & Vreeken, J Omen: Discovering Sequential Patterns with Reliable Prediction Delays. Knowledge and Information Systems vol.64(4), pp 1013-1045, Springer, 2022. (IF 2.822)project website
2021
Fischer, J & Vreeken, J Differentiable Pattern Set Mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 383-392, ACM, 2021. (15.4% acceptance rate)
Coupette, C & Vreeken, J Graph Similarity Description: How Are These Graphs Similar?. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 185-195, ACM, 2021. (15.4% acceptance rate)
Fischer, J, Oláh, A & Vreeken, J What's in the Box? Explaining Neural Networks with Robust Rules. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2021. (21.4% acceptance rate)project website
Budhathoki, K, Boley, M & Vreeken, J Discovering Reliable Causal Rules. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate)project website
Wiegand, B, Klakow, D & Vreeken, J Mining Easily Understandable Models from Complex Event Data. In: SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate)project website
Kalofolias, J, Welke, P & Vreeken, J SUSAN: The Structural Similarity Random Walk Kernel. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate)project website
Mian, OA, Marx, A & Vreeken, J Discovering Fully Oriented Causal Networks. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2021. (21.3% acceptance)project website
Schmidt, F, Marx, A, Baumgarten, N, Hebel, M, Wegner, M, Kaulich, M, Leisegang, M, Brandes, R, Göke, J, Vreeken, J & Schulz, MH Integrative Analysis of Epigenetics Data Identifies Gene-Specific Regulatory Elements. Nucleic Acids Research, Oxford University Press, 2021. (IF 16.97)
Dutta, A, Vreeken, J, Ghiringhelli, L & Bereau, T Data-driven Equation for Drug-Membrane Permeability across Drugs and Membranes. Journal of Chemical Physics vol.24(154), AIP, 2021. (IF 2.991)