Vreeken, Jilles

46 publications

NeurIPS 2025 Causal Mixture Models: Characterization and Discovery Sarah Mameche, Janis Kalofolias, Jilles Vreeken

AAAI 2025 Federated Binary Matrix Factorization Using Proximal Optimization Sebastian Dalleiger, Jilles Vreeken, Michael Kamp

AAAI 2025 From Your Block to Our Block: How to Find Shared Structure Between Stochastic Block Models over Multiple Graphs Iiro Kumpulainen, Sebastian Dalleiger, Jilles Vreeken, Nikolaj Tatti

AISTATS 2025 Information-Theoretic Causal Discovery in Topological Order Sascha Xu, Sarah Mameche, Jilles Vreeken

NeurIPS 2025 Neural Rule Lists: Learning Discretizations, Rules, and Order in One Go Sascha Xu, Nils Philipp Walter, Jilles Vreeken

AAAI 2025 SPACETIME: Causal Discovery from Non-Stationary Time Series Sarah Mameche, Lénaïg Cornanguer, Urmi Ninad, Jilles Vreeken

NeurIPS 2024 Causal Discovery from Event Sequences by Local Cause-Effect Attribution Joscha Cüppers, Sascha Xu, Ahmed Musa, Jilles Vreeken

ECML-PKDD 2024 Data Is Moody: Discovering Data Modification Rules from Process Event Logs Marco Bjarne Schuster, Boris Wiegand, Jilles Vreeken

AAAI 2024 Discovering Sequential Patterns with Predictable Inter-Event Delays Joscha Cüppers, Paul Krieger, Jilles Vreeken

AAAI 2024 Finding Interpretable Class-Specific Patterns Through Efficient Neural Search Nils Philipp Walter, Jonas Fischer, Jilles Vreeken

AISTATS 2024 Identifying Confounding from Causal Mechanism Shifts Sarah Mameche, Jilles Vreeken, David Kaltenpoth

ICML 2024 Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence Sascha Xu, Nils Philipp Walter, Janis Kalofolias, Jilles Vreeken

AAAI 2024 What Are the Rules? Discovering Constraints from Data Boris Wiegand, Dietrich Klakow, Jilles Vreeken

UAI 2023 Causal Discovery with Hidden Confounders Using the Algorithmic Markov Condition David Kaltenpoth, Jilles Vreeken

ICLR 2023 Federated Learning from Small Datasets Michael Kamp, Jonas Fischer, Jilles Vreeken

AAAI 2023 Identifying Selection Bias from Observational Data David Kaltenpoth, Jilles Vreeken

AAAI 2023 Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments Osman Mian, Michael Kamp, Jilles Vreeken

NeurIPS 2023 Learning Causal Models Under Independent Changes Sarah Mameche, David Kaltenpoth, Jilles Vreeken

ICML 2023 Nonlinear Causal Discovery with Latent Confounders David Kaltenpoth, Jilles Vreeken

AISTATS 2023 Nothing but Regrets — Privacy-Preserving Federated Causal Discovery Osman Mian, David Kaltenpoth, Michael Kamp, Jilles Vreeken

AAAI 2022 Differentially Describing Groups of Graphs Corinna Coupette, Sebastian Dalleiger, Jilles Vreeken

AAAI 2022 Discovering Interpretable Data-to-Sequence Generators Boris Wiegand, Dietrich Klakow, Jilles Vreeken

NeurIPS 2022 Efficiently Factorizing Boolean Matrices Using Proximal Gradient Descent Sebastian Dalleiger, Jilles Vreeken

ICML 2022 Inferring Cause and Effect in the Presence of Heteroscedastic Noise Sascha Xu, Osman A Mian, Alexander Marx, Jilles Vreeken

ICML 2022 Label-Descriptive Patterns and Their Application to Characterizing Classification Errors Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken

AAAI 2022 Naming the Most Anomalous Cluster in Hilbert Space for Structures with Attribute Information Janis Kalofolias, Jilles Vreeken

AAAI 2021 Discovering Fully Oriented Causal Networks Osman Mian, Alexander Marx, Jilles Vreeken

ICML 2021 What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules Jonas Fischer, Anna Olah, Jilles Vreeken

AAAI 2020 Explainable Data Decompositions Sebastian Dalleiger, Jilles Vreeken

IJCAI 2019 Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms Panagiotis Mandros, Mario Boley, Jilles Vreeken

ECML-PKDD 2019 Sets of Robust Rules, and How to Find Them Jonas Fischer, Jilles Vreeken

AISTATS 2019 Testing Conditional Independence on Discrete Data Using Stochastic Complexity Alexander Marx, Jilles Vreeken

ECML-PKDD 2018 Causal Inference on Multivariate and Mixed-Type Data Alexander Marx, Jilles Vreeken

ECML-PKDD 2016 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva Del Garda, Italy, September 19-23, 2016, Proceedings, Part I Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken

ECML-PKDD 2016 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva Del Garda, Italy, September 19-23, 2016, Proceedings, Part II Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken

ECML-PKDD 2015 Non-Parametric Jensen-Shannon Divergence Hoang Vu Nguyen, Jilles Vreeken

MLJ 2015 The Blind Men and the Elephant: On Meeting the Problem of Multiple Truths in Data from Clustering and Pattern Mining Perspectives Arthur Zimek, Jilles Vreeken

ECML-PKDD 2015 The Difference and the Norm - Characterising Similarities and Differences Between Databases Kailash Budhathoki, Jilles Vreeken

ICML 2014 Multivariate Maximal Correlation Analysis Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm

ECML-PKDD 2013 Detecting Bicliques in GF[q] Jan Ramon, Pauli Miettinen, Jilles Vreeken

ECML-PKDD 2013 Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, Tijl De Bie

ECML-PKDD 2012 Discovering Descriptive Tile Trees - By Mining Optimal Geometric Subtiles Nikolaj Tatti, Jilles Vreeken

ECML-PKDD 2011 Comparing Apples and Oranges - Measuring Differences Between Data Mining Results Nikolaj Tatti, Jilles Vreeken

ECML-PKDD 2011 MIME: A Framework for Interactive Visual Pattern Mining Bart Goethals, Sandy Moens, Jilles Vreeken

ECML-PKDD 2010 Summarising Data by Clustering Items Michael Mampaey, Jilles Vreeken

ECML-PKDD 2009 Identifying the Components Matthijs van Leeuwen, Jilles Vreeken, Arno Siebes