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ORCIDJilles Vreeken
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- affiliation: CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
- affiliation: Saarland University, Saarbrücken, Germany
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2020 – today
- 2023
[c97]David Kaltenpoth, Jilles Vreeken:
Identifying Selection Bias from Observational Data. AAAI 2023: 8177-8185- [c96]Osman Mian, Michael Kamp, Jilles Vreeken:
Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments. AAAI 2023: 9171-9179 - [c95]Osman Mian, David Kaltenpoth, Michael Kamp, Jilles Vreeken:
Nothing but Regrets - Privacy-Preserving Federated Causal Discovery. AISTATS 2023: 8263-8278 - [c94]Michael Kamp, Jonas Fischer, Jilles Vreeken:
Federated Learning from Small Datasets. ICLR 2023 - [c93]David Kaltenpoth, Jilles Vreeken:
Nonlinear Causal Discovery with Latent Confounders. ICML 2023: 15639-15654 - [c92]Joscha Cüppers
, Jilles Vreeken:
Below the Surface: Summarizing Event Sequences with Generalized Sequential Patterns. KDD 2023: 348-357 - [c91]David Kaltenpoth, Jilles Vreeken:
Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition. UAI 2023: 1016-1026 - [i38]Jonas Fischer, Rebekka Burkholz, Jilles Vreeken:
Preserving local densities in low-dimensional embeddings. CoRR abs/2301.13732 (2023) - [i37]Sebastian Dalleiger, Jilles Vreeken:
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. CoRR abs/2307.07615 (2023) - 2022
- [j25]Joscha Cüppers, Janis Kalofolias, Jilles Vreeken:
Omen: discovering sequential patterns with reliable prediction delays. Knowl. Inf. Syst. 64(4): 1013-1045 (2022) - [c90]Corinna Coupette, Sebastian Dalleiger, Jilles Vreeken:
Differentially Describing Groups of Graphs. AAAI 2022: 3959-3967 - [c89]Janis Kalofolias, Jilles Vreeken:
Naming the Most Anomalous Cluster in Hilbert Space for Structures with Attribute Information. AAAI 2022: 4057-4064 - [c88]Boris Wiegand, Dietrich Klakow, Jilles Vreeken:
Discovering Interpretable Data-to-Sequence Generators. AAAI 2022: 4237-4244 - [c87]Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken:
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors. ICML 2022: 8691-8707 - [c86]Sascha Xu, Osman Mian, Alexander Marx, Jilles Vreeken:
Inferring Cause and Effect in the Presence of Heteroscedastic Noise. ICML 2022: 24615-24630 - [c85]Sebastian Dalleiger, Jilles Vreeken:
Discovering Significant Patterns under Sequential False Discovery Control. KDD 2022: 263-272 - [c84]Sarah Mameche, David Kaltenpoth, Jilles Vreeken:
Discovering Invariant and Changing Mechanisms from Data. KDD 2022: 1242-1252 - [c83]Sebastian Dalleiger, Jilles Vreeken:
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. NeurIPS 2022 - [i36]Corinna Coupette, Sebastian Dalleiger, Jilles Vreeken:
Differentially Describing Groups of Graphs. CoRR abs/2201.04064 (2022) - [i35]Corinna Coupette, Jilles Vreeken, Bastian Rieck:
All the World's a (Hyper)Graph: A Data Drama. CoRR abs/2206.08225 (2022) - 2021
- [c82]Osman Mian, Alexander Marx, Jilles Vreeken:
Discovering Fully Oriented Causal Networks. AAAI 2021: 8975-8982 - [c81]Jonas Fischer, Anna Oláh, Jilles Vreeken:
What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules. ICML 2021: 3352-3362 - [c80]Corinna Coupette, Jilles Vreeken:
Graph Similarity Description: How Are These Graphs Similar? KDD 2021: 185-195 - [c79]Jonas Fischer, Jilles Vreeken:
Differentiable Pattern Set Mining. KDD 2021: 383-392 - [c78]Kailash Budhathoki, Mario Boley, Jilles Vreeken:
Discovering Reliable Causal Rules. SDM 2021: 1-9 - [c77]Boris Wiegand, Dietrich Klakow, Jilles Vreeken:
Mining Easily Understandable Models from Complex Event Logs. SDM 2021: 244-252 - [c76]Janis Kalofolias, Pascal Welke, Jilles Vreeken:
SUSAN: The Structural Similarity Random Walk Kernel. SDM 2021: 298-306 - [i34]Edith Heiter, Jonas Fischer, Jilles Vreeken:
Factoring out prior knowledge from low-dimensional embeddings. CoRR abs/2103.01828 (2021) - [i33]Alexander Marx, Jilles Vreeken:
Formally Justifying MDL-based Inference of Cause and Effect. CoRR abs/2105.01902 (2021) - [i32]Corinna Coupette, Jilles Vreeken:
Graph Similarity Description: How Are These Graphs Similar? CoRR abs/2105.14364 (2021) - [i31]Michael Kamp, Jonas Fischer, Jilles Vreeken:
Federated Learning from Small Datasets. CoRR abs/2110.03469 (2021) - [i30]Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken:
Label-Descriptive Patterns and their Application to Characterizing Classification Errors. CoRR abs/2110.09599 (2021) - 2020
- [j24]Panagiotis Mandros, Mario Boley, Jilles Vreeken:
Discovering dependencies with reliable mutual information. Knowl. Inf. Syst. 62(11): 4223-4253 (2020) - [c75]Sebastian Dalleiger, Jilles Vreeken:
Explainable Data Decompositions. AAAI 2020: 3709-3716 - [c74]Joscha Cüppers, Jilles Vreeken:
Just Wait For It... Mining Sequential Patterns with Reliable Prediction Delays. ICDM 2020: 82-91 - [c73]Sebastian Dalleiger, Jilles Vreeken:
The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery. ICDM 2020: 978-983 - [c72]Jonas Fischer, Jilles Vreeken:
Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity. KDD 2020: 813-823 - [c71]Frédéric Pennerath, Panagiotis Mandros, Jilles Vreeken:
Discovering Approximate Functional Dependencies using Smoothed Mutual Information. KDD 2020: 1254-1264 - [c70]Panagiotis Mandros, David Kaltenpoth, Mario Boley, Jilles Vreeken:
Discovering Functional Dependencies from Mixed-Type Data. KDD 2020: 1404-1414 - [c69]Yang Zhang, Mathias Humbert, Bartlomiej Surma, Praveen Manoharan, Jilles Vreeken, Michael Backes:
Towards Plausible Graph Anonymization. NDSS 2020 - [c68]Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra:
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. WWW 2020: 1115-1126 - [i29]Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra:
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. CoRR abs/2003.10412 (2020) - [i28]Kailash Budhathoki, Mario Boley, Jilles Vreeken:
Discovering Reliable Causal Rules. CoRR abs/2009.02728 (2020)
2010 – 2019
- 2019
- [j23]Alexander Marx, Jilles Vreeken:
Telling cause from effect by local and global regression. Knowl. Inf. Syst. 60(3): 1277-1305 (2019) - [j22]Matthijs van Leeuwen, Polo Chau, Jilles Vreeken, Dafna Shahaf, Christos Faloutsos:
Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12 Iss. 1). ACM Trans. Knowl. Discov. Data 13(1): 13:1-13:2 (2019) - [c67]Alexander Marx, Jilles Vreeken:
Testing Conditional Independence on Discrete Data using Stochastic Complexity. AISTATS 2019: 496-505 - [c66]Janis Kalofolias, Mario Boley, Jilles Vreeken:
Discovering Robustly Connected Subgraphs with Simple Descriptions. ICDM 2019: 1150-1155 - [c65]Panagiotis Mandros, Mario Boley, Jilles Vreeken:
Discovering Reliable Correlations in Categorical Data. ICDM 2019: 1252-1257 - [c64]Panagiotis Mandros, Mario Boley, Jilles Vreeken:
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms. IJCAI 2019: 6206-6210 - [c63]Alexander Marx, Jilles Vreeken:
Identifiability of Cause and Effect using Regularized Regression. KDD 2019: 852-861 - [c62]Jilles Vreeken, Kenji Yamanishi:
Modern MDL meets Data Mining Insights, Theory, and Practice. KDD 2019: 3229-3230 - [c61]Jonas Fischer, Jilles Vreeken:
Sets of Robust Rules, and How to Find Them. ECML/PKDD (1) 2019: 38-54 - [c60]David Kaltenpoth, Jilles Vreeken:
We Are Not Your Real Parents: Telling Causal from Confounded using MDL. SDM 2019: 199-207 - [i27]David Kaltenpoth, Jilles Vreeken:
We Are Not Your Real Parents: Telling Causal from Confounded using MDL. CoRR abs/1901.06950 (2019) - [i26]Nikolaj Tatti, Jilles Vreeken:
Finding Good Itemsets by Packing Data. CoRR abs/1902.02392 (2019) - [i25]Nikolaj Tatti, Jilles Vreeken:
The Long and the Short of It: Summarising Event Sequences with Serial Episodes. CoRR abs/1902.02834 (2019) - [i24]Nikolaj Tatti, Jilles Vreeken:
Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles. CoRR abs/1902.02861 (2019) - [i23]Nikolaj Tatti, Jilles Vreeken:
Comparing Apples and Oranges: Measuring Differences between Data Mining Results. CoRR abs/1902.07165 (2019) - [i22]Alexander Marx, Jilles Vreeken:
Testing Conditional Independence on Discrete Data using Stochastic Complexity. CoRR abs/1903.04829 (2019) - [i21]Michael Mampaey, Jilles Vreeken, Nikolaj Tatti:
Summarizing Data Succinctly with the Most Informative Itemsets. CoRR abs/1904.11134 (2019) - [i20]Panagiotis Mandros, Mario Boley, Jilles Vreeken:
Discovering Reliable Correlations in Categorical Data. CoRR abs/1908.11682 (2019) - 2018
- [j21]Andrea Hornáková, Markus List, Jilles Vreeken, Marcel H. Schulz:
JAMI: fast computation of conditional mutual information for ceRNA network analysis. Bioinform. 34(17): 3050-3051 (2018) - [j20]Kailash Budhathoki, Jilles Vreeken:
Origo: causal inference by compression. Knowl. Inf. Syst. 56(2): 285-307 (2018) - [j19]Hao Wu, Yue Ning, Prithwish Chakraborty, Jilles Vreeken, Nikolaj Tatti, Naren Ramakrishnan:
Generating Realistic Synthetic Population Datasets. ACM Trans. Knowl. Discov. Data 12(4): 45:1-45:22 (2018) - [c59]Panagiotis Mandros, Mario Boley, Jilles Vreeken:
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms. ICDM 2018: 317-326 - [c58]Kailash Budhathoki, Jilles Vreeken:
Accurate Causal Inference on Discrete Data. ICDM 2018: 881-886 - [c57]Danai Koutra, Jilles Vreeken, Francesco Bonchi:
Summarizing Graphs at Multiple Scales: New Trends. ICDM 2018: 1097 - [c56]Alexander Marx, Jilles Vreeken:
Causal Inference on Multivariate and Mixed-Type Data. ECML/PKDD (2) 2018: 655-671 - [c55]Kailash Budhathoki, Jilles Vreeken:
Causal Inference on Event Sequences. SDM 2018: 55-63 - [i19]Alexander Marx, Jilles Vreeken:
Causal Discovery by Telling Apart Parents and Children. CoRR abs/1808.06356 (2018) - [i18]Panagiotis Mandros, Mario Boley, Jilles Vreeken:
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms. CoRR abs/1809.05467 (2018) - 2017
- [j18]Andrea K. Fischer, Jilles Vreeken, Dietrich Klakow:
Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL. Computación y Sistemas 21(4) (2017) - [j17]Mario Boley, Bryan R. Goldsmith, Luca M. Ghiringhelli, Jilles Vreeken:
Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery. Data Min. Knowl. Discov. 31(5): 1391-1418 (2017) - [c54]Janis Kalofolias, Mario Boley, Jilles Vreeken:
Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups. ICDM 2017: 197-206 - [c53]Alexander Marx, Jilles Vreeken:
Telling Cause from Effect Using MDL-Based Local and Global Regression. ICDM 2017: 307-316 - [c52]Kailash Budhathoki, Jilles Vreeken:
MDL for Causal Inference on Discrete Data. ICDM 2017: 751-756 - [c51]Panagiotis Mandros, Mario Boley, Jilles Vreeken:
Discovering Reliable Approximate Functional Dependencies. KDD 2017: 355-363 - [c50]Roel Bertens, Jilles Vreeken, Arno Siebes:
Efficiently Discovering Unexpected Pattern-Co-Occurrences. SDM 2017: 126-134 - [c49]Kailash Budhathoki, Jilles Vreeken:
Correlation by Compression. SDM 2017: 525-533 - [c48]Robert S. Pienta, Minsuk Kahng, Zhiyuan Lin, Jilles Vreeken, Partha P. Talukdar, James Abello, Ganesh Parameswaran, Duen Horng Chau:
FACETS: Adaptive Local Exploration of Large Graphs. SDM 2017: 597-605 - [c47]Apratim Bhattacharyya, Jilles Vreeken:
Efficiently Summarising Event Sequences with Rich Interleaving Patterns. SDM 2017: 795-803 - [i17]Mario Boley, Bryan R. Goldsmith, Luca M. Ghiringhelli, Jilles Vreeken:
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery. CoRR abs/1701.07696 (2017) - [i16]Apratim Bhattacharyya, Jilles Vreeken:
Efficiently Summarising Event Sequences with Rich Interleaving Patterns. CoRR abs/1701.08096 (2017) - [i15]Alexander Marx, Jilles Vreeken:
Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length. CoRR abs/1702.06385 (2017) - [i14]Kailash Budhathoki, Jilles Vreeken:
Causal Inference by Stochastic Complexity. CoRR abs/1702.06776 (2017) - [i13]Panagiotis Mandros, Mario Boley, Jilles Vreeken:
Discovering Reliable Approximate Functional Dependencies. CoRR abs/1705.09391 (2017) - [i12]Janis Kalofolias, Mario Boley, Jilles Vreeken:
Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups. CoRR abs/1709.07941 (2017) - [i11]Yang Zhang, Mathias Humbert, Bartlomiej Surma, Praveen Manoharan, Jilles Vreeken, Michael Backes:
CTRL+Z: Recovering Anonymized Social Graphs. CoRR abs/1711.05441 (2017) - 2016
- [j16]Kumaripaba Athukorala, Dorota Glowacka, Giulio Jacucci, Antti Oulasvirta, Jilles Vreeken:
Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks. J. Assoc. Inf. Sci. Technol. 67(11): 2635-2651 (2016) - [c46]Kailash Budhathoki, Jilles Vreeken:
Causal Inference by Compression. ICDM 2016: 41-50 - [c45]Roel Bertens, Jilles Vreeken, Arno Siebes:
Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns. KDD 2016: 735-744 - [c44]Polina Rozenshtein, Aristides Gionis, B. Aditya Prakash, Jilles Vreeken:
Reconstructing an Epidemic Over Time. KDD 2016: 1835-1844 - [c43]Hoang Vu Nguyen, Jilles Vreeken:
Flexibly Mining Better Subgroups. SDM 2016: 585-593 - [c42]Hoang Vu Nguyen, Panagiotis Mandros, Jilles Vreeken:
Universal Dependency Analysis. SDM 2016: 792-800 - [c41]Hoang Vu Nguyen, Jilles Vreeken:
Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series. SDM 2016: 828-836 - [e4]Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I. Lecture Notes in Computer Science 9851, Springer 2016, ISBN 978-3-319-46127-4 [contents] - [e3]Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II. Lecture Notes in Computer Science 9852, Springer 2016, ISBN 978-3-319-46226-4 [contents] - [i10]Hao Wu, Yue Ning, Prithwish Chakraborty, Jilles Vreeken, Nikolaj Tatti, Naren Ramakrishnan:
Generating Realistic Synthetic Population Datasets. CoRR abs/1602.06844 (2016) - [i9]Hao Wu, Maoyuan Sun, Jilles Vreeken, Nikolaj Tatti, Chris North, Naren Ramakrishnan:
Interactive and Iterative Discovery of Entity Network Subgraphs. CoRR abs/1608.03889 (2016) - 2015
- [j15]Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Klemens Böhm:
Erratum to: Unsupervised interaction-preserving discretization of multivariate data. Data Min. Knowl. Discov. 29(1): 296-297 (2015) - [j14]Arthur Zimek, Jilles Vreeken:
The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives. Mach. Learn. 98(1-2): 121-155 (2015) - [j13]Danai Koutra, U Kang, Jilles Vreeken, Christos Faloutsos:
Summarizing and understanding large graphs. Stat. Anal. Data Min. 8(3): 183-202 (2015) - [c40]Hoang Vu Nguyen, Jilles Vreeken:
Non-parametric Jensen-Shannon Divergence. ECML/PKDD (2) 2015: 173-189 - [c39]Kailash Budhathoki, Jilles Vreeken:
The Difference and the Norm - Characterising Similarities and Differences Between Databases. ECML/PKDD (2) 2015: 206-223 - [c38]Sanjar Karaev, Pauli Miettinen, Jilles Vreeken:
Getting to Know the Unknown Unknowns: Destructive-Noise Resistant Boolean Matrix Factorization. SDM 2015: 325-333 - [c37]Shashidhar Sundareisan, Jilles Vreeken, B. Aditya Prakash:
Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics. SDM 2015: 415-423 - [c36]Jilles Vreeken:
Causal Inference by Direction of Information. SDM 2015: 909-917 - [i8]Robert S. Pienta, Zhiyuan Lin, Minsuk Kahng, Jilles Vreeken, Partha P. Talukdar, James Abello, Ganesh Parameswaran, Duen Horng Chau:
Seeing the Forest through the Trees: Adaptive Local Exploration of Large Graphs. CoRR abs/1505.06792 (2015) - [i7]Hoang Vu Nguyen, Jilles Vreeken:
Canonical Divergence Analysis. CoRR abs/1510.08370 (2015) - [i6]Hoang Vu Nguyen, Jilles Vreeken:
Flexibly Mining Better Subgroups. CoRR abs/1510.08382 (2015) - [i5]Hoang Vu Nguyen, Jilles Vreeken:
Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series. CoRR abs/1510.08385 (2015) - [i4]Hoang Vu Nguyen, Jilles Vreeken:
Universal Dependency Analysis. CoRR abs/1510.08389 (2015) - [i3]Roel Bertens, Jilles Vreeken, Arno Siebes:
Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences. CoRR abs/1512.07048 (2015) - [i2]Roel Bertens, Jilles Vreeken, Arno Siebes:
Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns. CoRR abs/1512.07056 (2015) - 2014
- [j12]Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Klemens Böhm:
Unsupervised interaction-preserving discretization of multivariate data. Data Min. Knowl. Discov. 28(5-6): 1366-1397 (2014) - [j11]Hao Wu, Jilles Vreeken, Nikolaj Tatti, Naren Ramakrishnan:
Uncovering the plot: detecting surprising coalitions of entities in multi-relational schemas. Data Min. Knowl. Discov. 28(5-6): 1398-1428 (2014) - [j10]B. Aditya Prakash, Jilles Vreeken, Christos Faloutsos:
Efficiently spotting the starting points of an epidemic in a large graph. Knowl. Inf. Syst. 38(1): 35-59 (2014) - [j9]Pauli Miettinen, Jilles Vreeken:
MDL4BMF: Minimum Description Length for Boolean Matrix Factorization. ACM Trans. Knowl. Discov. Data 8(4): 18:1-18:31 (2014) - [c35]Kumaripaba Athukorala, Antti Oulasvirta, Dorota Glowacka, Jilles Vreeken, Giulio Jacucci:
Narrow or Broad?: Estimating Subjective Specificity in Exploratory Search. CIKM 2014: 819-828 - [c34]Erdal Kuzey, Jilles Vreeken, Gerhard Weikum:
A Fresh Look on Knowledge Bases: Distilling Named Events from News. CIKM 2014: 1689-1698 - [c33]Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm:
Multivariate Maximal Correlation Analysis. ICML 2014: 775-783 - [c32]Danai Koutra, U Kang, Jilles Vreeken, Christos Faloutsos:
VOG: Summarizing and Understanding Large Graphs. SDM 2014: 91-99