Publications per year


2024

Cueppers, J, Krieger, P & Vreeken, J Discovering Sequential Patterns with Predictable Inter-Event Delays. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2024. (23.8% acceptance rate)project website
Mameche, S, Vreeken, J & Kaltenpoth, D Identifying Confounding from Causal Mechanism Shifts. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2024. (27.6% acceptance rate)project website
Walter, N, Fischer, J & Vreeken, J Finding Interpretable Class-Specific Patterns through Efficient Neural Search. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2024. (23.8% acceptance rate)project website
Wiegand, B, Klakow, D & Vreeken, J What are the Rules? Discovering Constraints from Data. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2024. (oral presentation, 2,3% acceptance rate; 23.8% overall)project website
Mian, OA & Mameche, S An Information Theoretic Framework for Continual Learning of Causal Networks. In: Proceedings of the AAAI 2024 Continual Causality Bridge Program, PMLR, 2024.
Xu, S, Cueppers, J & Vreeken, J Succinct Interaction-Aware Explanations. Technical Report 2402.05566, arXiv, 2024.
Xu, S, Walter, N, Kalofolias, J & Vreeken, J Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence. Technical Report 2402.12930, arXiv, 2024.

2023

Coupette, C, Vreeken, J & Rieck, B All the World's a (Hyper)Graph: A Data Drama. Digital Scholarship in the Humanities, pp 1-23, Oxford Academic Press, 2023. (IF 0.8)
Coupette, C, Dalleiger, S & Rieck, B Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework. In: Proceedings of the International Conference on Representation Learning (ICLR), OpenReview, 2023. (31.8% acceptance rate)
Cueppers, J & Vreeken, J Below the Surface: Summarizing Event Sequences with Generalized Sequential Patterns. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2023. (22.1% acceptance rate)project website
Kaltenpoth, D & Vreeken, J Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition. In: Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI), AUAI, 2023. (31.2% acceptance rate)project website
Kaltenpoth, D & Vreeken, J Nonlinear Causal Discovery with Latent Confounders. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2023. (27.9% acceptance rate)project website
Kaltenpoth, D & Vreeken, J Identifying Selection Bias from Observational Data. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp 8177-8185, AAAI, 2023. (oral presentation, 10.8% acceptance rate; 19.6% overall)project website
Kamp, M, Fischer, J & Vreeken, J Federated Learning from Small Datasets. In: Proceedings of the International Conference on Representation Learning (ICLR), OpenReview, 2023. (31.8% acceptance rate)project website
Mameche, S, Kaltenpoth, D & Vreeken, J Learning Causal Models under Independent Changes. In: Proceedings of Neural Information Processing Systems (NeurIPS), PMRL, 2023. (26.1% acceptance rate)project website
Mian, O, Kaltenpoth, D, Kamp, M & Vreeken, J Nothing but Regrets — Privacy-Preserving Federated Causal Discovery. In: Proceedings of the 26nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2023. (29% acceptance rate)project website
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), pp 9171-9179, AAAI, 2023. (19.6% acceptance rate)project website
Shani, C, Vreeken, J & Shahaf, D Towards Concept-Aware Large Language Models. In: Findings of the Association for Computational Linguistics (EMNLP Findings), ACL, 2023.
Wiegand, B, Klakow, D & Vreeken, J Why Are We Waiting? Discovering Interpretable Models for Predicting Sojourn and Waiting Times. In: SIAM International Conference on Data Mining (SDM), SIAM, 2023. (27.4% acceptance rate)project website
Schuster, MB, Wiegand, B & Vreeken, J Data is Moody: Discovering Data Modification Rules from Process Event Logs. Technical Report 2312.14571, arXiv, 2023.project website
Dalleiger, S Characteristics and Commonalities - Differentially Describing Datasets with Insightful Patterns. Dissertation, Saarland University, 2023.
Aadil, M Causal Inductive Biases for Domain Generalization. M.Sc. Thesis, Saarland University, 2023.
George, N Hessian Regularisation for Disentangling Neural Networks. M.Sc. Thesis, Saarland University, 2023.
Krieger, PN Mining Sequential Patterns with Gap Distributions. M.Sc. Thesis, Saarland University, 2023.
Gasanov, R Information-Theoretic Causal Discovery with Latent Confounders. M.Sc. Thesis, Saarland University, 2023.
Schuster, M Discovering Data Modification Rules. M.Sc. Thesis, Saarland University, 2023.
Singh, J Pattern Mining for Multiple Interleaving Labels. M.Sc. Thesis, Saarland University, 2023.
Yaseen, M VAEs with Structured Latent Concepts. M.Sc. Thesis, Saarland University, 2023.
Bauerschmidt, T What makes you say that? Explaining Transformers using Rule Mining and Input Prototyping. B.Sc. Thesis, Saarland University, 2023.
Chatzianastasiou, R-M Best of Both Worlds – Greedy Equivalence Search with an Inequivalent Score. B.Sc. Thesis, Saarland University, 2023.

2022

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
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
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
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
Fischer, J & Burkholz, R Plant'n'Seek: Can You Find the Winning Ticket?. In: Proceedings of the International Conference on Representation Learning (ICLR), OpenReview, 2022.
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
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
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)
Marx, A & Fischer, J Estimating Mutual Information via Geodesic kNN. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2022.
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
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
Marx, A & Vreeken, J Formally Justifying MDL-based Inference of Cause and Effect. In: Proceedings of the AAAI Workshop on Information Theoretic Causal Inference and Discovery (ITCI'22), 2022.
Mian, O, Kaltenpoth, D & Kamp, M Regret-based Federated Causal Discovery. In: Proceedings of the ACM SIGKDD Workshop on Causal Discovery, PMLR, 2022.project website
Xu, S, Marx, A, Mian, O & Vreeken, J Causal Inference with Heteroscedastic Noise Models. In: Proceedings of the AAAI Workshop on Information Theoretic Causal Inference and Discovery (ITCI'22), 2022.
Coupette, C, Vreeken, J & Rieck, B All the World's a (Hyper)Graph: A Data Drama. Technical Report 2206.08225, arXiv, 2022.
Kalofolias, J Subgroup Discovery for Structured Target Concepts. Dissertation, Saarland University, 2022.
Arkhangelskaya, E Disentangling Neural Networks with Block Diagonal Coefficient. M.Sc. Thesis, Saarland University, 2022.
Gassner, M Best Enjoyed Together: Mining Dependent Patterns. M.Sc. Thesis, Saarland University, 2022.
Khan, SA Benchmarking XAI Methods using Causal Graphs. M.Sc. Thesis, Saarland University, 2022.

2021

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)
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)
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
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
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)
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
Marx, A, Gretton, A & Mooij, J A Weaker Faithfulness Assumption based on Triple Interactions. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI'21), AUAI, 2021. (26.5% acceptance rate)
Marx, A, Yang, L & Van Leeuwen, M Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate)
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
Petzka, H, Kamp, M, Adilova, L, Sminchisescu, C & Boley, M Relative Flatness and Generalization. In: Proceedings of Advances in Neural Information Processing Systems (NeurIPS), PMLR, 2021. (26.2% acceptance rate)
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
Coupette, C, Singh, J & Spamann, H Simplify Your Law: Using Information Theory to Deduplicate Legal Documents. In: Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW 2021), pp 631-638, IEEE, 2021.
Mandros, P Discovering Robust Dependencies from Data. Dissertation, Saarland University, 2021.
Marx, A Information-Theoretic Causal Discovery. Dissertation, Saarland University, 2021.
Bruxmeier, T Stable, Diverse, and Efficient Markov Blanket Discovery. M.Sc. Thesis, Saarland University, 2021.
Ezema, A Discovering Robust Rules that Express Positive and Negative Associations in Data. M.Sc. Thesis, Saarland University, 2021.
Hess, J Where are my Parents? Causal Discovery Under a Weaker Faithfulness Assumption. M.Sc. Thesis, Saarland University, 2021.
Hinrichs, F Mining Memorable Motifs from Music. M.Sc. Thesis, Saarland University, 2021.
Mameche, S Causal Inference from Different Contexts using Algorithmic Causal Models. M.Sc. Thesis, Saarland University, 2021.
Kindler, D Density Preserving t-SNE. B.Sc. Thesis, Saarland University, 2021.
Wilms, M GPU-accelerated Branch-and-Bound with Applications in High-Dimensional Markov Blanket Discovery. B.Sc. Thesis, Saarland University, 2021.

2020

Mandros, P, Boley, M & Vreeken, J Discovering Dependencies with Reliable Mutual Information. Knowledge and Information Systems vol.62, pp 4223-4253, Springer, 2020. (IF 2.936)project website
Sutton, C, Boley, M, Ghiringhelli, L, Rupp, M, Vreeken, J & Scheffler, M Identifying Domains of Applicability of Machine Learning Models for Materials Science. Nature Communications vol.11(4428), pp 1-9, Nature Research, 2020. (IF 12.12)
Belth, C, Zheng, X, Vreeken, J & Koutra, D What is Normal, What is Strange, and What is Missing in a Knowledge Graph. In: Proceedings of the Web Conference (WWW), ACM, 2020. (oral presentation; overall acceptance rate 19.2%)
Cueppers, J & Vreeken, J Just Wait For It... Mining Sequential Patterns with Reliable Prediction Delays. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'20), IEEE, 2020. (full paper, 9.8% acceptance rate; overall 19.7%) (invited for the KAIS Special Issue on the Best of IEEE ICDM 2020)project website
Dalleiger, S & Vreeken, J The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'20), IEEE, 2020. (19.7% acceptance rate)project website
Dalleiger, S & Vreeken, J Explainable Data Decompositions. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'20), AAAI, 2020. (oral presentation 4.5% acceptance rate; overall 20.6%)project website
Fischer, J & Vreeken, J Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity . In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2020. (16.8% acceptance rate)project website
Mandros, P, Kaltenpoth, D, Boley, M & Vreeken, J Discovering Functional Dependencies from Mixed-Type Data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2020. (16.8% acceptance rate)project website
Penerath, F, Mandros, P & Vreeken, J Discovering Approximate Functional Dependencies using Smoothed Mutual Information . In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2020. (16.8% acceptance rate)
Zhang, Y, Humbert, M, Surma, B, Manoharan, P, Vreeken, J & Backes, M Towards Plausible Graph Anonymization. In: Proceedings of the Network and Distributed System Security Symposium (NDSS), The Internet Society, 2020. (17.4% acceptance rate)
Dutta, A, Vreeken, J, Ghiringhelli, LM & Bereau, T Data-driven equation for drug-membrane permeability across drugs and membranes. Technical Report 2012.01766, arXiv, 2020.
Marx, A, Gretton, A & Mooij, J A Weaker Faithfulness Assumption based on Triple Interactions. Technical Report 2010.14265, arXiv, 2020.
Budhathoki, K Causal Inference on Discrete Data. Dissertation, Saarland University, 2020.
Heiter, E Factoring out prior knowledge from low-dimensional embeddings. M.Sc. Thesis, Saarland University, 2020.
Oláh, A What's in the Box? Explaining Neural Networks with Robust Rules. M.Sc. Thesis, Saarland University, 2020.
Sukarieh, S SPRAP: Detecting Opinion Spam Campaigns in Online Rating Services. M.Sc. Thesis, Saarland University, 2020.

2019

Marx, A & Vreeken, J Telling Cause from Effect by Local and Global Regression. Knowledge and Information Systems vol.60(3), pp 1277-1305, IEEE, 2019. (IF 2.397)project website
Fischer, J & Vreeken, J Sets of Robust Rules, and How to Find Them. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Data (ECMLPKDD), Springer, 2019. (17.7% acceptance rate)project website
Kalofolias, J, Boley, M & Vreeken, J Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), IEEE, 2019. (18.5% acceptance rate)project website
Kaltenpoth, D & Vreeken, J We Are Not Your Real Parents: Telling Causal From Confounded by MDL. In: SIAM International Conference on Data Mining (SDM), SIAM, 2019. (22.9% acceptance rate)project website
Mandros, P, Boley, M & Vreeken, J Discovering Reliable Correlations in Categorical Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'19), IEEE, 2019. (18.5% acceptance rate)project website
Mandros, P, Boley, M & Vreeken, J Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms (Extended Abstract). In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), IJCAI, 2019. (Invited contribution to the IJCAI Sister Conference Best Paper Track)project website
Marx, A & Vreeken, J Identifiability of Cause and Effect using Regularized Regression. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2019. (oral presentation 9.2% acceptance rate; overall 14.2%)project website
Marx, A & Vreeken, J Testing Conditional Independence on Discrete Data using Stochastic Complexity. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2019. (31% acceptance rate)project website
Kalofolias, J, Boley, M & Vreeken, J Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the ECMLPKDD Workshop on Graph Embedding and Mining (GEM), 2019. (oral presentation, 21% acceptance rate)project website
Kalofolias, J, Boley, M & Vreeken, J Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the ACM SIGKDD Workshop on Mining and Learning from Graphs (MLG), 2019.project website
Marx, A & Vreeken, J Approximating Algorithmic Conditional Independence for Discrete Data. In: Proceedings of the the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI, AAAI, 2019.project website
Saran, D & Vreeken, J Summarizing Dynamic Graphs using MDL. In: Proceedings of the ECMLPKDD Workshop on Graph Embedding and Mining (GEM), 2019. (oral presentation, 21% acceptance rate)
Vreeken, J & Tatti, N (eds) Proceedings of the ACM SIGKDD Workshop on Learning and Mining for Cybersecurity (LEMINCS). , 2019.external project website
Cotop, SA How to be Grim: Explaining Data at Different Granularity Levels. M.Sc. Thesis, Saarland University, 2019.
Cueppers, J How to Make Cake: Finding Causal Patterns for Marked Events in Sequences. M.Sc. Thesis, Saarland University, 2019.project website
Mian, OA Causal Discovery using MDL-based Regression. M.Sc. Thesis, Saarland University, 2019.
Saran, D Summarizing Dynamic Graphs using MDL. M.Sc. Thesis, Saarland University, 2019.

2018

Budhathoki, K & Vreeken, J Origo: Causal Inference by Compression. Knowledge and Information Systems vol.56(2), pp 285-307, Springer, 2018. (IF 2.247)project website
List, M, Hornakova, A, Vreeken, J & Schulz, MH JAMI — Fast computation of Conditional Mutual Information for ceRNA network analysis. Bioinformatics vol.34(17), pp 3050-3051, Oxford University Press, 2018. (IF 7.307)
Wu, H, Ning, Y, Chakraborty, P, Vreeken, J, Tatti, N & Ramakrishnan, N Generating Realistic Synthetic Population Datasets. Transactions on Knowledge Discovery from Data vol.12(4), pp 1-45, ACM, 2018. (IF 1.68)
Budhathoki, K & Vreeken, J Accurate Causal Inference on Discrete Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'18), IEEE, 2018. (19.9% acceptance rate)project website
Budhathoki, K & Vreeken, J Causal Inference on Event Sequences. In: Proceedings of the SIAM Conference on Data Mining (SDM), pp 55-63, SIAM, 2018. (23.2% acceptance rate)project website
Mandros, P, Boley, M & Vreeken, J Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'18), IEEE, 2018. (full paper, 8.9% acceptance rate; overall 19.9%) (Best Paper Award)project website
Marx, A & Vreeken, J Causal Inference on Multivariate and Mixed Type Data. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Data (ECMLPKDD), Springer, 2018. (25% acceptance rate)project website
Budhathoki, K, Boley, M & Vreeken, J Rule Discovery for Exploratory Causal Reasoning. In: Proceedings of the NeurIPS 2018 workshop on Causal Learning, pp 1-14, 2018.
Marx, A & Vreeken, J Stochastic Complexity for Testing Conditional Independence on Discrete Data. In: Proceedings of the NeurIPS 2018 workshop on Causal Learning, pp 1-12, 2018.project website
Marx, A & Vreeken, J Causal Discovery by Telling Apart Parents and Children. Technical Report 1808.06356, arXiv, 2018.project website
Wiegand, B Optimization of Work Roll Change Intervals through Data-Driven Roll-Wear Models at the Finishing Stand of a Four-High Rolling Mill. M.Sc. Thesis, Saarland University, 2018.
Farag, I Efficiently Summarising Data with Patterns that Overlap. M.Sc. Thesis, Saarland University, 2018.
Halbe, M OctOPUS: Branch-and-Bound Search with both Sibling Propagations and Closure Operators. M.Sc. Thesis, Saarland University, 2018.
Aburahma, M Smoothie: Smoothing Discrete Data. M.Sc. Thesis, Saarland University, 2018.
Eissfeller, M Reverse-Engineering Epidemics in Large Weighted Graphs. M.Sc. Thesis, Saarland University, 2018.
Dembelova, T More Robust Interaction Preserving Discretization. M.Sc. Thesis, Saarland University, 2018.
Brendel, Y Reconstructing Dependency Networks by Cumulate Entropy Estimation. M.Sc. Thesis, Saarland University, 2018.

2017

Boley, M, Goldsmith, BR, Ghiringhelli, LM & Vreeken, J Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery. Data Mining and Knowledge Discovery vol.31(5), pp 1391-1418, Springer, 2017. (IF 3.160) (ECML PKDD'17 Journal Track)
Fischer, AK, Vreeken, J & Klakow, D Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL. Computación y Sistemas vol.21(4), 2017. (Special Issue for the 18th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing'17)
Goldsmith, B, Boley, M, Vreeken, J, Scheffler, M & Ghiringhelli, L Uncovering Structure-Property Relationships of Materials by Subgroup Discovery. New Journal of Physics vol.19, IOP Publishing Ltd and Deutsche Physikalische Gesellschaft, 2017. (IF 3.57) (Included in the NJP Highlights of 2017)
Bertens, R, Vreeken, J & Siebes, A Efficiently Discovering Unexpected Pattern-Co-Occurrences. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 126-134, SIAM, 2017. (25% acceptance rate)project websiteslides
Bhattacharyya, A & Vreeken, J Efficiently Summarising Event Sequences with Rich Interleaving Patterns. In: Proceedings of the SIAM Conference on Data Mining (SDM), pp 795-803, SIAM, 2017. (selected in the top 10 papers of SDM'17, 2.7% acceptance rate; overall 25%)project website
Budhathoki, K & Vreeken, J MDL for Causal Inference on Discrete Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'17), pp 751-756, IEEE, 2017. (19.9% acceptance rate)project website
Budhathoki, K & Vreeken, J Correlation by Compression. In: Proceedings of the SIAM Conference on Data Mining (SDM), SIAM, 2017. (25% acceptance rate)project website
Kalofolias, J, Boley, M & Vreeken, J Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'17), IEEE, 2017. (full paper, 9.3% acceptance rate; overall 19.9%)project website
Mandros, P, Boley, M & Vreeken, J Discovering Reliable Approximate Functional Dependencies. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp 355-363, ACM, 2017. (oral presentation, 8.6% acceptance rate; overall 17.5%)project website
Marx, A & Vreeken, J Telling Cause from Effect by MDL-based Local and Global Regression. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'17), pp 307-316, IEEE, 2017. (full paper, 9.3% acceptance rate; overall 19.9%) (invited for the KAIS Special Issue on the Best of IEEE ICDM 2017)project website
Pienta, R, Kahng, M, Lin, Z, Vreeken, J, Talukdar, P, Abello, J, Parameswaran, G & Chau, DH Adaptive Local Exploration of Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 597-605, SIAM, 2017. (25% acceptance rate)project website
Grosse, K & Vreeken, J Summarising Event Sequences using Serial Episodes and an Ontology. In: Proceedings of the 4th Workshop on Interactions between Data Mining and Natural Language Processing (DMNLP'17), pp 33-48, CEUR Workshop Proceedings, 2017.
Hinrichs, F & Vreeken, J Characterising the Difference and the Norm between Sequences Databases. In: Proceedings of the 4th Workshop on Interactions between Data Mining and Natural Language Processing (DMNLP'17), pp 49-64, CEUR Workshop Proceedings, 2017.
Bhattacharyya, A & Vreeken, J Efficiently Summarising Event Sequences with Rich Interleaving Patterns. Technical Report 1701.08096, arXiv, 2017.project website
Hättasch, B Automated Ontology Refinement using Compression-based Learning. M.Sc. Thesis, Technische Universität Darmstadt, 2017.
Jilke, H Explore: Discovering Power-Law Communities in Large Graphs. M.Sc. Thesis, Saarland University, 2017.
Burghartz, R Compress it with fire: adaptive codes for MDL-based pattern mining. M.Sc. Thesis, Saarland University, 2017.
Hinrichs, F Finding Difference and Norm between Sequence Databases. B.Sc. Thesis, Saarland University, 2017.

2016

Athukorala, K, Glowacka, D, Jacucci, G, Oulasvirta, A & Vreeken, J Is Exploratory Search Different? A Comparison of Information Search Behavior for Exploratory and Lookup Tasks. Journal of the Association for Information Science and Technology (JASIST) vol.67(11), pp 2635-2651, Wiley, 2016. (IF 2.26)
Bertens, R, Vreeken, J & Siebes, A Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16), pp 735-744, ACM, 2016. (oral presentation, 8.9% acceptance rate; overall 18.1%)project websitevideo recording
Budhathoki, K & Vreeken, J Causal Inference by Compression. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'16), IEEE, 2016. (full paper, 8.5% acceptance rate; overall 19.6%) (invited for the KAIS Special Issue on the Best of IEEE ICDM 2016)
Kalofolias, J, Galbrun, E & Miettinen, P From Sets of Good Redescriptions to Good Sets of Redescriptions. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), IEEE, 2016. (full paper, 8.5% acceptance rate; overall 19.6%) (invited for the KAIS Special Issue on the Best of IEEE ICDM 2016)
Nguyen, H-V, Mandros, P & Vreeken, J Universal Dependency Analysis. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 792-800, SIAM, 2016. (overall 25% acceptance rate)project websiteslides
Nguyen, H-V & Vreeken, J Flexibly Mining Better Subgroups. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 585-593, SIAM, 2016. (overall 25% acceptance rate)project websiteslides
Nguyen, H-V & Vreeken, J Linear-time Detection of Non-Linear Changes in Massively High Dimensional Time Series. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 828-836, SIAM, 2016. (overall 25% acceptance rate)project websiteslides
Rozenshtein, P, Gionis, A, Prakash, BA & Vreeken, J Reconstructing an Epidemic over Time. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp 1835-1844, ACM, 2016. (18.1% acceptance rate)project website
Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA). , 2016.external project website
Frasconi, P, Landwehr, N, Manco, G & Vreeken, J (eds) Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data (ECMLPKDD). Springer, 2016. (Part I)external project website
Frasconi, P, Landwehr, N, Manco, G & Vreeken, J (eds) Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data (ECMLPKDD). Springer, 2016. (Part II)external project website
Baradaranshahroudi, A Fast Computation of Highest Correlated Segments in Multivariate Time-Series. M.Sc. Thesis, Saarland University, 2016.
Bhattacharyya, A Squish: Efficiently Summarising Sequences with Rich and Interleaving Patterns. M.Sc. Thesis, Saarland University, 2016.
Wójciak, BA Spaghetti: Finding Storylines in Large Collections of Documents. M.Sc. Thesis, Saarland University, 2016.
Grosse, K An Approach for Ontological Pattern-based Summarization. M.Sc. Thesis, Saarland University, 2016.
Gandhi, M Towards Summarising Large Transaction Databases. M.Sc. Thesis, Saarland University, 2016.
Salyaeva, M Summarising and Recommending with Skipisodes. M.Sc. Thesis, Saarland University, 2016.
Halbe, M Skim: Alternative Candidate Selections for Slim through Sketching. B.Sc. Thesis, Saarland University, 2016.

2015

Koutra, D, Kang, U, Vreeken, J & Faloutsos, C Summarizing and Understanding Large Graphs. Statistical Analysis and Data Mining vol.8(3), pp 183-202, Wiley, 2015.project website
Zimek, A & Vreeken, J The Blind Men and the Elephant: About Meeting the Problem of Multiple Truths in Data from Clustering and Pattern Mining Perspectives. Machine Learning vol.98(1), pp 121-155, Springer, 2015. (IF 1.587)
Budhathoki, K & Vreeken, J The Difference and the Norm – Characterising Similarities and Differences between Databases. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 206-223, Springer, 2015.project websiteslides
Karaev, S, Miettinen, P & Vreeken, J Getting to Know the Unknown Unknowns: Destructive-Noise Resistant Boolean Matrix Factorization. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 325-333, SIAM, 2015.
Nguyen, H-V & Vreeken, J Non-Parametric Jensen-Shannon Divergence. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 173-189, Springer, 2015.project website
Pienta, R, Lin, Z, Kahng, M, Vreeken, J, Talukdar, PP, Abello, J, Parameswaran, G & Chau, DH AdaptiveNav: Adaptive Discovery of Interesting and Surprising Nodes in Large Graphs. In: Proceedings of the IEEE Conference on Visualization (VIS), IEEE, 2015.project websitevideo recording
Sundareisan, S, Vreeken, J & Prakash, BA Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 415-423, SIAM, 2015.
Vreeken, J Causal Inference by Direction of Information. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 909-917, SIAM, 2015.project website
Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA). , 2015.external project website
Budhathoki, K Correlation by Compression. M.Sc. Thesis, Saarland University, 2015.
Mandros, P Information-Theoretic Supervised Feature Selection for Continuous Data. M.Sc. Thesis, Saarland University, 2015.

2014

Miettinen, P & Vreeken, J mdl4bmf: Minimal Description Length for Boolean Matrix Factorization. Transactions on Knowledge Discovery from Data vol.8(4), pp 1-30, ACM, 2014. (IF 1.68)
Nguyen, H-V, Müller, E, Vreeken, J & Böhm, K Unsupervised Interaction-Preserving Discretization of Multivariate Data. Data Mining and Knowledge Discovery vol.28(5), pp 1366-1397, Springer, 2014. (IF 2.877) (ECML PKDD'14 Journal Track)
Prakash, BA, Vreeken, J & Faloutsos, C Efficiently Spotting the Starting Points of an Epidemic in a Large Graph. Knowledge and Information Systems vol.38(1), pp 35-59, Springer, 2014. (IF 2.225)
Webb, G & Vreeken, J Efficient Discovery of the Most Interesting Associations. Transactions on Knowledge Discovery from Data vol.8(3), pp 1-31, ACM, 2014. (IF 1.68)implementation
Wu, H, Vreeken, J, Tatti, N & Ramakrishnan, N Uncovering the Plot: Detecting Surprising Coalitions of Entities in Multi-Relational Schemas. Data Mining and Knowledge Discovery vol.28(5), pp 1398-1428, Springer, 2014. (IF 2.877) (ECML PKDD'14 Journal Track)project websiteslides
Athukorala, K, Oulasvirta, A, Glowacka, D, Vreeken, J & Jaccuci, G Narrow or Broad? Estimating Subjective Specificity in Exploratory Search. In: Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp 819-828, ACM, 2014. (IR track full paper, overall 21% acceptance rate)
Koutra, D, Kang, U, Vreeken, J & Faloutsos, C VoG: Summarizing and Understanding Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 91-99, SIAM, 2014. (fast track journal invitation, as one of the best of SDM'14; full paper with presentation, 15.4% acceptance rate)project websiteslides
Kuzey, E, Vreeken, J & Weikum, G A Fresh Look on Knowledge Bases: Distilling Named Events from News. In: Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp 1689-1698, ACM, 2014. (KM track full paper, overall 21% acceptance rate)
Nguyen, H-V, Müller, E, Vreeken, J & Böhm, K Multivariate Maximal Correlation Analysis. In: Proceedings of the International Conference on Machine Learning (ICML), pp 775-783, JMLR: W&CP vol.32, 2014. (25.0% acceptance rate)project website
Vreeken, J & Tatti, N Interesting Patterns. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 105-134, pp 105-134, Springer, 2014.
Zimek, A, Assent, I & Vreeken, J Frequent Pattern Mining Algorithms for Data Clustering. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 403-424, pp 403-424, Springer, 2014.
van Leeuwen, M & Vreeken, J Mining and Using Sets of Patterns through Compression. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 165-198, pp 165-198, Springer, 2014.
Athukorala, K, Oulasvirta, A, Glowacka, D, Vreeken, J & Jacucci, G Supporting Exploratory Search Through User Modeling. In: Proceedings of the UMAP Joint Workshop on Personalized Information Access (PIA), pp 1-6, 2014.
Athukorala, K, Oulasvirta, A, Glowacka, D, Vreeken, J & Jacucci, G Interaction Model to Predict Subjective-Specificity of Search Results. In: Proceedings of the 22nd Conference on User Modeling, Adaptation and Personalization — Late-Breaking Results (UMAP), pp 1-6, 2014.
Gandhi, M & Vreeken, J Slimmer, outsmarting Slim. PhD Poster and Video at: the 13th International Symposium on Intelligent Data Analysis (IDA), Springer, 2014.
Chau, DH, Vreeken, J, van Leeuwen, M & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA). , 2014.external project website
Bier, S Causal Inference by Packing Data. B.Sc. Thesis, Saarland University, 2014.

2013

Akoglu, L, Vreeken, J, Tong, H, Chau, DH, Tatti, N & Faloutsos, C Mining Connection Pathways for Marked Nodes in Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 37-45, SIAM, 2013. (oral presentation, 14.4% acceptance rate; overal 25%)
Akşehirli, E, Goethals, B, Müller, E & Vreeken, J Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 937-942, IEEE, 2013. (19.6% acceptance rate)
Kontonasios, K-N, Vreeken, J & De Bie, T Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 256-271, Springer, 2013.
Nguyen, H-V, Müller, E, Vreeken, J, Keller, F & Böhm, K CMI: An Information-Theoretic Contrast Measure for Enhancing Subspace Cluster and Outlier Detection. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 198-206, SIAM, 2013. (oral presentation, 14.4% acceptance rate; overal 25%)
Ramon, J, Miettinen, P & Vreeken, J Detecting Bicliques in GF[q]. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 509-524, Springer, 2013.
Chau, DH, Vreeken, J, van Leeuwen, M & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA). ACM, 2013.external project website