Compression Picks Item Sets That Matter. - Matthijs van Leeuwen; Jilles Vreeken; Arno Siebes (PKDD, 2006)

Item Sets that Compress. - Arno Siebes; Jilles Vreeken; Matthijs van Leeuwen (SDM, 2006)

Preserving Privacy through Data Generation. - Jilles Vreeken; Matthijs van Leeuwen; Arno Siebes (ICDM, 2007)

Characterising the difference. - Jilles Vreeken; Matthijs van Leeuwen; Arno Siebes (KDD, 2007)

Finding Good Itemsets by Packing Data. - Nikolaj Tatti; Jilles Vreeken (ICDM, 2008)

Filling in the Blanks - Krimp Minimisation for Missing Data. - Jilles Vreeken; Arno Siebes (ICDM, 2008)

Low-Entropy Set Selection. - Hannes Heikinheimo; Jilles Vreeken; Arno Siebes; Heikki Mannila (SDM, 2009)

Identifying the components. - Matthijs van Leeuwen; Jilles Vreeken; Arno Siebes (Data Min. Knowl. Discov., 2009)

Making Pattern Mining Useful. - Jilles Vreeken (2009)

Summarising Data by Clustering Items. - Michael Mampaey; Jilles Vreeken (ECML/PKDD (2), 2010)

Making pattern mining useful. - Jilles Vreeken (SIGKDD Explor., 2010)

Useful patterns (UP'10) ACM SIGKDD workshop report. - Jilles Vreeken; Nikolaj Tatti; Bart Goethals (SIGKDD Explor., 2010)

Maximum Entropy Modelling for Assessing Results on Real-Valued Data. - Kleanthis-Nikolaos Kontonasios; Jilles Vreeken; Tijl De Bie (ICDM, 2011)

MIME: a framework for interactive visual pattern mining. - Bart Goethals; Sandy Moens; Jilles Vreeken (KDD, 2011)

Tell me what i need to know: succinctly summarizing data with itemsets. - Michael Mampaey; Nikolaj Tatti; Jilles Vreeken (KDD, 2011)

Model order selection for boolean matrix factorization. - Pauli Miettinen; Jilles Vreeken (KDD, 2011)

When Pattern Met Subspace Cluster. - Jilles Vreeken; Arthur Zimek (MultiClust@ECML/PKDD, 2011)

Comparing Apples and Oranges - Measuring Differences between Data Mining Results. - Nikolaj Tatti; Jilles Vreeken (ECML/PKDD (3), 2011)

The Odd One Out: Identifying and Characterising Anomalies. - Koen Smets; Jilles Vreeken (SDM, 2011)

Krimp: mining itemsets that compress. - Jilles Vreeken; Matthijs van Leeuwen; Arno Siebes (Data Min. Knowl. Discov., 2011)

Interactively and Visually Exploring Tours of Marked Nodes in Large Graphs. - Duen Horng Chau; Leman Akoglu; Jilles Vreeken; Hanghang Tong; Christos Faloutsos (ASONAM, 2012)

Fast and reliable anomaly detection in categorical data. - Leman Akoglu; Hanghang Tong; Jilles Vreeken; Christos Faloutsos (CIKM, 2012)

Spotting Culprits in Epidemics: How Many and Which Ones? - B. Aditya Prakash; Jilles Vreeken; Christos Faloutsos (ICDM, 2012)

TourViz: interactive visualization of connection pathways in large graphs. - Duen Horng Chau; Leman Akoglu; Jilles Vreeken; Hanghang Tong; Christos Faloutsos (KDD, 2012)

The long and the short of it: summarising event sequences with serial episodes. - Nikolaj Tatti; Jilles Vreeken (KDD, 2012)

Discovering Descriptive Tile Trees - By Mining Optimal Geometric Subtiles. - Nikolaj Tatti; Jilles Vreeken (ECML/PKDD (1), 2012)

Slim: Directly Mining Descriptive Patterns. - Koen Smets; Jilles Vreeken (SDM, 2012)

Comparing apples and oranges: measuring differences between exploratory data mining results. - Nikolaj Tatti; Jilles Vreeken (Data Min. Knowl. Discov., 2012)

Summarizing data succinctly with the most informative itemsets. - Michael Mampaey; Jilles Vreeken; Nikolaj Tatti (ACM Trans. Knowl. Discov. Data, 2012)

Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data. - Emin Aksehirli; Bart Goethals; Emmanuel Müller; Jilles Vreeken (ICDM, 2013)

Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data. - Kleanthis-Nikolaos Kontonasios; Jilles Vreeken; Tijl De Bie (ECML/PKDD (2), 2013)

Detecting Bicliques in GF[q]. - Jan Ramon; Pauli Miettinen; Jilles Vreeken (ECML/PKDD (1), 2013)

Mining Connection Pathways for Marked Nodes in Large Graphs. - Leman Akoglu; Duen Horng Chau; Christos Faloutsos; Nikolaj Tatti; Hanghang Tong; Jilles Vreeken (SDM, 2013)

CMI: An Information-Theoretic Contrast Measure for Enhancing Subspace Cluster and Outlier Detection. - Klemens Böhm; Fabian Keller; Emmanuel Müller; Hoang Vu Nguyen; Jilles Vreeken (SDM, 2013)

Summarizing categorical data by clustering attributes. - Michael Mampaey; Jilles Vreeken (Data Min. Knowl. Discov., 2013)

Efficient Discovery of the Most Interesting Associations. - Geoffrey I. Webb; Jilles Vreeken (ACM Trans. Knowl. Discov. Data, 2013)

Mining and Using Sets of Patterns through Compression. - Matthijs van Leeuwen; Jilles Vreeken (Frequent Pattern Mining, 2014)

Interesting Patterns. - Jilles Vreeken; Nikolaj Tatti (Frequent Pattern Mining, 2014)

Frequent Pattern Mining Algorithms for Data Clustering. - Arthur Zimek; Ira Assent; Jilles Vreeken (Frequent Pattern Mining, 2014)

Narrow or Broad?: Estimating Subjective Specificity in Exploratory Search. - Kumaripaba Athukorala; Antti Oulasvirta; Dorota Glowacka; Jilles Vreeken; Giulio Jacucci (CIKM, 2014)

A Fresh Look on Knowledge Bases: Distilling Named Events from News. - Erdal Kuzey; Jilles Vreeken; Gerhard Weikum (CIKM, 2014)

Multivariate Maximal Correlation Analysis. - Hoang Vu Nguyen; Emmanuel Müller; Jilles Vreeken; Pavel Efros; Klemens Böhm (ICML, 2014)

VOG: Summarizing and Understanding Large Graphs. - Danai Koutra; U Kang; Jilles Vreeken; Christos Faloutsos (SDM, 2014)

Interaction Model to Predict Subjective-Specificity of Search Results. - Kumaripaba Athukorala; Antti Oulasvirta; Dorota Glowacka; Jilles Vreeken; Giulio Jacucci (UMAP Workshops, 2014)

Supporting Exploratory Search Through User Modeling. - Kumaripaba Athukorala; Antti Oulasvirta; Dorota Glowacka; Jilles Vreeken; Giulio Jacucci (UMAP Workshops, 2014)

Unsupervised interaction-preserving discretization of multivariate data. - Hoang Vu Nguyen; Emmanuel Müller; Jilles Vreeken; Klemens Böhm (Data Min. Knowl. Discov., 2014)

Uncovering the plot: detecting surprising coalitions of entities in multi-relational schemas. - Hao Wu 041; Jilles Vreeken; Nikolaj Tatti; Naren Ramakrishnan (Data Min. Knowl. Discov., 2014)

Efficiently spotting the starting points of an epidemic in a large graph. - B. Aditya Prakash; Jilles Vreeken; Christos Faloutsos (Knowl. Inf. Syst., 2014)

MDL4BMF: Minimum Description Length for Boolean Matrix Factorization. - Pauli Miettinen; Jilles Vreeken (ACM Trans. Knowl. Discov. Data, 2014)

The Difference and the Norm - Characterising Similarities and Differences Between Databases. - Kailash Budhathoki; Jilles Vreeken (ECML/PKDD (2), 2015)

Non-parametric Jensen-Shannon Divergence. - Hoang Vu Nguyen; Jilles Vreeken (ECML/PKDD (2), 2015)

Getting to Know the Unknown Unknowns: Destructive-Noise Resistant Boolean Matrix Factorization. - Sanjar Karaev; Pauli Miettinen; Jilles Vreeken (SDM, 2015)

Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics. - Shashidhar Sundareisan; Jilles Vreeken; B. Aditya Prakash (SDM, 2015)

Causal Inference by Direction of Information. - Jilles Vreeken (SDM, 2015)

Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences. - Roel Bertens; Jilles Vreeken; Arno Siebes (CoRR, 2015)

Canonical Divergence Analysis. - Hoang Vu Nguyen; Jilles Vreeken (CoRR, 2015)

Universal Dependency Analysis. - Hoang Vu Nguyen; Jilles Vreeken (CoRR, 2015)

Seeing the Forest through the Trees: Adaptive Local Exploration of Large Graphs. - Robert S. Pienta; Zhiyuan Lin 001; Minsuk Kahng; Jilles Vreeken; Partha P. Talukdar; James Abello; Ganesh Parameswaran; Duen Horng Chau (CoRR, 2015)

Erratum to: Unsupervised interaction-preserving discretization of multivariate data. - Hoang Vu Nguyen; Emmanuel Müller; Jilles Vreeken; Klemens Böhm (Data Min. Knowl. Discov., 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 (Mach. Learn., 2015)

Summarizing and understanding large graphs. - Danai Koutra; U Kang; Jilles Vreeken; Christos Faloutsos (Stat. Anal. Data Min., 2015)

Causal Inference by Compression. - Kailash Budhathoki; Jilles Vreeken (ICDM, 2016)

Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns. - Roel Bertens; Jilles Vreeken; Arno Siebes (KDD, 2016)

Reconstructing an Epidemic Over Time. - Polina Rozenshtein; Aristides Gionis; B. Aditya Prakash; Jilles Vreeken (KDD, 2016)

Universal Dependency Analysis. - Hoang Vu Nguyen; Panagiotis Mandros; Jilles Vreeken (SDM, 2016)

Flexibly Mining Better Subgroups. - Hoang Vu Nguyen; Jilles Vreeken (SDM, 2016)

Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series. - Hoang Vu Nguyen; Jilles Vreeken (SDM, 2016)

Interactive and Iterative Discovery of Entity Network Subgraphs. - Hao Wu 041; Maoyuan Sun; Jilles Vreeken; Nikolaj Tatti; Chris North 001; Naren Ramakrishnan (CoRR, 2016)

Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks. - Kumaripaba Athukorala; Dorota Glowacka; Giulio Jacucci; Antti Oulasvirta; Jilles Vreeken (J. Assoc. Inf. Sci. Technol., 2016)

MDL for Causal Inference on Discrete Data. - Kailash Budhathoki; Jilles Vreeken (ICDM, 2017)

Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups. - Janis Kalofolias; Mario Boley; Jilles Vreeken (ICDM, 2017)

Telling Cause from Effect Using MDL-Based Local and Global Regression. - Alexander Marx 001; Jilles Vreeken (ICDM, 2017)

Discovering Reliable Approximate Functional Dependencies. - Panagiotis Mandros; Mario Boley; Jilles Vreeken (KDD, 2017)

Efficiently Discovering Unexpected Pattern-Co-Occurrences. - Roel Bertens; Jilles Vreeken; Arno Siebes (SDM, 2017)

Efficiently Summarising Event Sequences with Rich Interleaving Patterns. - Apratim Bhattacharyya; Jilles Vreeken (SDM, 2017)

Correlation by Compression. - Kailash Budhathoki; Jilles Vreeken (SDM, 2017)

FACETS: Adaptive Local Exploration of Large Graphs. - Robert S. Pienta; Minsuk Kahng; Zhiyuan Lin 001; Jilles Vreeken; Partha P. Talukdar; James Abello; Ganesh Parameswaran; Duen Horng Chau (SDM, 2017)

Causal Inference by Stochastic Complexity. - Kailash Budhathoki; Jilles Vreeken (CoRR, 2017)

Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length. - Alexander Marx 001; Jilles Vreeken (CoRR, 2017)

CTRL+Z: Recovering Anonymized Social Graphs. - Yang Zhang 016; Mathias Humbert; Bartlomiej Surma; Praveen Manoharan 001; Jilles Vreeken; Michael Backes 001 (CoRR, 2017)

Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL. - Andrea K. Fischer; Jilles Vreeken; Dietrich Klakow (Computación y Sistemas, 2017)

Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery. - Mario Boley; Bryan R. Goldsmith; Luca M. Ghiringhelli; Jilles Vreeken (Data Min. Knowl. Discov., 2017)

Accurate Causal Inference on Discrete Data. - Kailash Budhathoki; Jilles Vreeken (ICDM, 2018)

Summarizing Graphs at Multiple Scales: New Trends. - Danai Koutra; Jilles Vreeken; Francesco Bonchi (ICDM, 2018)

Causal Inference on Multivariate and Mixed-Type Data. - Alexander Marx 001; Jilles Vreeken (ECML/PKDD (2), 2018)

Causal Inference on Event Sequences. - Kailash Budhathoki; Jilles Vreeken (SDM, 2018)

JAMI: fast computation of conditional mutual information for ceRNA network analysis. - Andrea Hornáková; Markus List; Jilles Vreeken; Marcel H. Schulz (Bioinform., 2018)

Causal Discovery by Telling Apart Parents and Children. - Alexander Marx 001; Jilles Vreeken (CoRR, 2018)

Origo: causal inference by compression. - Kailash Budhathoki; Jilles Vreeken (Knowl. Inf. Syst., 2018)

Generating Realistic Synthetic Population Datasets. - Hao Wu 041; Yue Ning 001; Prithwish Chakraborty; Jilles Vreeken; Nikolaj Tatti; Naren Ramakrishnan (ACM Trans. Knowl. Discov. Data, 2018)

Testing Conditional Independence on Discrete Data using Stochastic Complexity. - Alexander Marx 001; Jilles Vreeken (AISTATS, 2019)

Discovering Robustly Connected Subgraphs with Simple Descriptions. - Janis Kalofolias; Mario Boley; Jilles Vreeken (ICDM, 2019)

Discovering Reliable Correlations in Categorical Data. - Panagiotis Mandros; Mario Boley; Jilles Vreeken (ICDM, 2019)

Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms. - Panagiotis Mandros; Mario Boley; Jilles Vreeken (IJCAI, 2019)

Identifiability of Cause and Effect using Regularized Regression. - Alexander Marx 001; Jilles Vreeken (KDD, 2019)

Modern MDL meets Data Mining Insights, Theory, and Practice. - Jilles Vreeken; Kenji Yamanishi (KDD, 2019)

Sets of Robust Rules, and How to Find Them. - Jonas Fischer; Jilles Vreeken (ECML/PKDD (1), 2019)

We Are Not Your Real Parents: Telling Causal from Confounded using MDL. - David Kaltenpoth; Jilles Vreeken (SDM, 2019)

Telling cause from effect by local and global regression. - Alexander Marx 001; Jilles Vreeken (Knowl. Inf. Syst., 2019)

Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12 Iss. 1). - Matthijs van Leeuwen; Polo Chau; Jilles Vreeken; Dafna Shahaf; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2019)

Explainable Data Decompositions. - Sebastian Dalleiger; Jilles Vreeken (AAAI, 2020)

Just Wait For It... Mining Sequential Patterns with Reliable Prediction Delays. - Joscha Cüppers; Jilles Vreeken (ICDM, 2020)

The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery. - Sebastian Dalleiger; Jilles Vreeken (ICDM, 2020)

Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity. - Jonas Fischer; Jilles Vreeken (KDD, 2020)

Discovering Functional Dependencies from Mixed-Type Data. - Panagiotis Mandros; David Kaltenpoth; Mario Boley; Jilles Vreeken (KDD, 2020)

Discovering Approximate Functional Dependencies using Smoothed Mutual Information. - Frédéric Pennerath; Panagiotis Mandros; Jilles Vreeken (KDD, 2020)

Towards Plausible Graph Anonymization. - Yang Zhang 016; Mathias Humbert; Bartlomiej Surma; Praveen Manoharan 001; Jilles Vreeken; Michael Backes 001 (NDSS, 2020)

What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. - Caleb Belth; Xinyi Zheng; Jilles Vreeken; Danai Koutra (WWW, 2020)

Discovering dependencies with reliable mutual information. - Panagiotis Mandros; Mario Boley; Jilles Vreeken (Knowl. Inf. Syst., 2020)

Discovering Fully Oriented Causal Networks. - Osman Mian; Alexander Marx 001; Jilles Vreeken (AAAI, 2021)

What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules. - Jonas Fischer; Anna Oláh; Jilles Vreeken (ICML, 2021)

Graph Similarity Description: How Are These Graphs Similar? - Corinna Coupette; Jilles Vreeken (KDD, 2021)

Differentiable Pattern Set Mining. - Jonas Fischer; Jilles Vreeken (KDD, 2021)

Discovering Reliable Causal Rules. - Kailash Budhathoki; Mario Boley; Jilles Vreeken (SDM, 2021)

SUSAN: The Structural Similarity Random Walk Kernel. - Janis Kalofolias; Pascal Welke; Jilles Vreeken (SDM, 2021)

Mining Easily Understandable Models from Complex Event Logs. - Boris Wiegand; Dietrich Klakow; Jilles Vreeken (SDM, 2021)

Factoring out prior knowledge from low-dimensional embeddings. - Edith Heiter; Jonas Fischer; Jilles Vreeken (CoRR, 2021)

Formally Justifying MDL-based Inference of Cause and Effect. - Alexander Marx 001; Jilles Vreeken (CoRR, 2021)

Graph Similarity Description: How Are These Graphs Similar? (Paper Replication Code). - Corinna Coupette; Jilles Vreeken (Zenodo, 2021)

Differentially Describing Groups of Graphs. - Corinna Coupette; Sebastian Dalleiger; Jilles Vreeken (AAAI, 2022)

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

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

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

Inferring Cause and Effect in the Presence of Heteroscedastic Noise. - Sascha Xu; Osman Mian; Alexander Marx 001; Jilles Vreeken (ICML, 2022)

Discovering Significant Patterns under Sequential False Discovery Control. - Sebastian Dalleiger; Jilles Vreeken (KDD, 2022)

Discovering Invariant and Changing Mechanisms from Data. - Sarah Mameche; David Kaltenpoth; Jilles Vreeken (KDD, 2022)

Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. - Sebastian Dalleiger; Jilles Vreeken (NeurIPS, 2022)

Omen: discovering sequential patterns with reliable prediction delays. - Joscha Cüppers; Janis Kalofolias; Jilles Vreeken (Knowl. Inf. Syst., 2022)

Differentially Describing Groups of Graphs (Paper Replication Code). - Corinna Coupette; Sebastian Dalleiger; Jilles Vreeken (Zenodo, 2022)

Differentially Describing Groups of Graphs (Paper Replication Code). - Corinna Coupette; Sebastian Dalleiger; Jilles Vreeken (Zenodo, 2022)

Hyperbard (Dataset). - Corinna Coupette; Jilles Vreeken; Bastian Rieck (Zenodo, 2022)

Hyperbard (Code). - Corinna Coupette; Jilles Vreeken; Bastian Rieck (Zenodo, 2022)

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 (AAAI, 2023)

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

Towards Concept-Aware Large Language Models. - Chen Shani; Jilles Vreeken; Dafna Shahaf (EMNLP (Findings), 2023)

Federated Learning from Small Datasets. - Michael Kamp; Jonas Fischer; Jilles Vreeken (ICLR, 2023)

Nonlinear Causal Discovery with Latent Confounders. - David Kaltenpoth; Jilles Vreeken (ICML, 2023)

Below the Surface: Summarizing Event Sequences with Generalized Sequential Patterns. - Joscha Cüppers; Jilles Vreeken (KDD, 2023)

Learning Causal Models under Independent Changes. - Sarah Mameche; David Kaltenpoth; Jilles Vreeken (NeurIPS, 2023)

Why Are We Waiting? Discovering Interpretable Models for Predicting Sojourn and Waiting Times. - Boris Wiegand; Dietrich Klakow; Jilles Vreeken (SDM, 2023)

Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition. - David Kaltenpoth; Jilles Vreeken (UAI, 2023)

Preserving local densities in low-dimensional embeddings. - Jonas Fischer; Rebekka Burkholz; Jilles Vreeken (CoRR, 2023)

Understanding and Mitigating Classification Errors Through Interpretable Token Patterns. - Michael A. Hedderich; Jonas Fischer; Dietrich Klakow; Jilles Vreeken (CoRR, 2023)

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 (AAAI, 2024)

What Are the Rules? Discovering Constraints from Data. - Boris Wiegand; Dietrich Klakow; Jilles Vreeken (AAAI, 2024)

Identifying Confounding from Causal Mechanism Shifts. - Sarah Mameche; Jilles Vreeken; David Kaltenpoth (AISTATS, 2024)

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

Learning Causal Networks from Episodic Data. - Osman Mian; Sarah Mameche; Jilles Vreeken (KDD, 2024)

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

Data is Moody: Discovering Data Modification Rules from Process Event Logs. - Marco Bjarne Schuster; Boris Wiegand; Jilles Vreeken (ECML/PKDD (2), 2024)

Succint Interaction-Aware Explanations. - Sascha Xu; Joscha Cüppers; Jilles Vreeken (CoRR, 2024)

The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective. - Nils Philipp Walter; Linara Adilova; Jilles Vreeken; Michael Kamp (CoRR, 2024)

Neuro-Symbolic Rule Lists. - Sascha Xu; Nils Philipp Walter; Jilles Vreeken (CoRR, 2024)

All the world's a (hyper)graph: A data drama. - Corinna Coupette; Jilles Vreeken; Bastian Rieck (Digit. Scholarsh. Humanit., 2024)

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 (AAAI, 2025)

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

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

Succinct Interaction-Aware Explanations. - Sascha Xu; Joscha Cüppers; Jilles Vreeken (KDD (1), 2025)

Accurately Estimating Unreported Infections using Information Theory. - Jiaming Cui; Bijaya Adhikari; Arash Haddadan; A. S. M. Ahsan-Ul-Haque; Jilles Vreeken; Anil Vullikanti; B. Aditya Prakash (SDM, 2025)

Now you see me! A framework for obtaining class-relevant saliency maps. - Nils Philipp Walter; Jilles Vreeken; Jonas Fischer (CoRR, 2025)

Seqret: Mining Rule Sets from Event Sequences. - Aleena Siji; Joscha Cüppers; Osman Ali Mian; Jilles Vreeken (CoRR, 2025)