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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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%)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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%)

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)

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)

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)

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)

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)

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)

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)

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)

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%)

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)

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)

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)

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)

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)

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)

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%)

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)

Budhathoki, K & Vreeken, J Correlation by Compression. In: Proceedings of the SIAM Conference on Data Mining (SDM), SIAM, 2017. (25% acceptance rate)

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%)

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%)

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)

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)

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%)

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)

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)

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)

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)

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)

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.

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.

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.

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.

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)

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)

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.

Akoglu, L, Tong, H, Vreeken, J & Faloutsos, C Fast and Reliable Anomaly Detection in Categoric Data. In: Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp 415-424, ACM, 2012. (full paper, 13.4% acceptance rate; 27% overall)

Prakash, BA, Vreeken, J & Faloutsos, C Spotting Culprits in Epidemics: How many and Which ones?. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 11-20, IEEE, 2012. (full paper, 10.7% acceptance rate; overall 20%)

Smets, K & Vreeken, J Slim: Directly Mining Descriptive Patterns. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 236-247, SIAM, 2012. (oral presentation, 14.6% acceptance rate)

Tatti, N & Vreeken, J Discovering Descriptive Tile Trees by Fast Mining of Optimal Geometric Subtiles. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 9-24, Springer, 2012.

Tatti, N & Vreeken, J The Long and the Short of It: Summarising Event Sequences with Serial Episodes. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 462-470, ACM, 2012. (17.6% acceptance rate)

Kontonasios, K-N, Vreeken, J & De Bie, T Maximum Entropy Modelling for Assessing Results on Real-Valued Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 350-359, IEEE, 2011. (oral presentation, 12.3% acceptance rate; overall 18%)

Mampaey, M, Tatti, N & Vreeken, J Data Summarization with Informative Itemsets. In: Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC), ISSN 1568-7805, 2011. (extended abstract of our KDD'11 paper)

Mampaey, M, Tatti, N & Vreeken, J Tell Me What I Need To Know: Succinctly Summarizing Data with Itemsets. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 573-581, ACM, 2011. (Best Student Paper Award; oral presentation, 7.8% acceptance rate; overall 17.5%)

Miettinen, P & Vreeken, J Model Order Selection for Boolean Matrix Factorization. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 51-59, ACM, 2011. (oral presentation, 7.8% acceptance rate; overall 17.5%)

Smets, K & Vreeken, J Identifying and Characterising Anomalies in Transaction Data. In: Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC), ISSN 1568-7805, 2011. (extended abstract of our SDM'11 paper)

Smets, K & Vreeken, J The Odd One Out: Identifying and Characterising Anomalies. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 804-815, SIAM, 2011. (25% acceptance rate)

Tatti, N & Vreeken, J Comparing Apples and Oranges – Measuring Differences between Data Mining Results. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 398-413, Springer, 2011. (invited for extension for best-of special issue, 3% acceptance rate; overall 20%)

Mampaey, M & Vreeken, J Summarising Data by Clustering Items. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 321-336, Springer, 2010. (18% acceptance rate)

Heikinheimo, H, Vreeken, J, Siebes, A & Mannila, H Low-Entropy Set Selection. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 569-579, SIAM, 2009. (25% acceptance rate)

van Leeuwen, M, Vreeken, J & Siebes, A Identifying the Components. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 32-32, Springer, 2009. (ECMLPKDD'09 Best Student Paper)

Tatti, N & Vreeken, J Finding Good Itemsets by Packing Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 588-597, IEEE, 2008. (9.8% acceptance rate)

Vreeken, J & Siebes, A Filling in the Blanks – Krimp Minimisation for Missing Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 1067-1072, IEEE, 2008. (19% acceptance rate)

Siebes, A, van Leeuwen, M & Vreeken, J MDL for Pattern Mining. In: Proceedings of the International Conference on Statistics for Data Mining, Learning and Knowledge Extraction Models (IASC), 2007.

Vreeken, J, van Leeuwen, M & Siebes, A Preserving Privacy through Data Generation. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 685-690, IEEE, 2007. (19% acceptance rate)

Vreeken, J, van Leeuwen, M & Siebes, A Characterising the Difference. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp 765-774, ACM, 2007. (19% acceptance rate)

Siebes, A, Vreeken, J & van Leeuwen, M Item Sets That Compress. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 393-404, SIAM, 2006. (16% acceptance rate)

van Leeuwen, M, Vreeken, J & Siebes, A Compression Picks the Item Sets that Matter. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 585-592, Springer, 2006. (18% acceptance rate)

Wiering, M, Vreeken, J, van Veenen, J & Koopman, ACM Simulation and Optimization of Traffic in a City. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp 453-458, IEEE, 2004.

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.

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.

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)

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.

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.

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)

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.

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.

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.

Akoglu, L, Vreeken, J, Tong, H, Chau, DH & Faloutsos, C Mining and Visualizing Connection Pathways in Large Information Networks. In: Proceedings of the Workshop on Information in Networks (WIN), pp 1-3, 2012.

Vreeken, J & Tatti, N Summarising Event Sequences with Serial Episodes. In: Proceedings of the 5th Workshop on Information Theoretic Methods in Science and Engineering (WITMSE), pp 82-85, 2012. (invited contribution, extended abstract of our KDD'12 paper)

Wu, H, Mampaey, M, Tatti, N, Vreeken, J, Hossain, MS & Ramakrishnan, N Where Do I Start? Algorithmic Strategies to Guide Intelligence Analysts. In: Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics (ISI-KDD), pp 1-8, ACM, 2012.

Chau, DH, Akoglu, L, Vreeken, J, Tong, H & Faloutsos, C Interactively and Visually Exploring Tours of Marked Nodes in Large Graphs. Demo at, and included in: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), ACM, 2012.

Chau, DH, Akoglu, L, Vreeken, J, Tong, H & Faloutsos, C TourViz: Interactive Visualization of Connection Pathways in Large Graphs. Demo at, and included in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 1516-1519, ACM, 2012.

Vreeken, J & Zimek, A When Pattern Met Subspace Cluster - A Relationship Story. In: Proceedings of the 2nd Workshop on Discovering, Summarizing and Using Multiple Clusterings (MultiClust), pp 7-18, 2011.

Goethals, B, Moens, S & Vreeken, J mime: A Framework for Interactive Visual Pattern Mining. Demo at, and included in: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 634-637, Springer, 2011.

Goethals, B, Moens, S & Vreeken, J mime: A Framework for Interactive Visual Pattern Mining. Demo at, and included in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 757-760, ACM, 2011.

Koopman, ACM, van Leeuwen, M & Vreeken, J Exploring Temporal Memory of LSTM and Spiking Circuits. In: Workshop on the Future of Neural Networks (FUNN), 2003.