Jilles Vreeken
118 publications
15 venues
H Index 29
Affiliation
CISPA Helmholtz Center for Information Security, Saarbr cken, GermanySaarland University, Saarbr cken, Germany
Links
DBLPGoogle Scholar
Twitter- https://cispa.de/en/people/jilles.vreeken
- http://people.mmci.uni-saarland.de/~jilles/
- https://openreview.net/profile?id=~Jilles_Vreeken2
- https://dl.acm.org/profile/81335499054
- https://zbmath.org/authors/?q=ai:vreeken.jilles
- https://mathscinet.ams.org/mathscinet/MRAuthorID/820091
- https://mathgenealogy.org/id.php?id=140616
- https://orcid.org/0000-0002-2310-2806
- https://www.wikidata.org/entity/Q98951967
| Name | Venue | Year | citations |
|---|---|---|---|
Causal Discovery from Interval-Based Event Sequences.
|
AAAI | 2026 | 0 |
| Information-Theoretic Causal Discovery in Topological Order.
|
AISTATS | 2025 | 6 |
| SPACETIME: Causal Discovery from Non-Stationary Time Series.
|
AAAI | 2025 | 3 |
| Accurately Estimating Unreported Infections using Information Theory.
|
SDM | 2025 | 0 |
| Federated Binary Matrix Factorization Using Proximal Optimization.
|
AAAI | 2025 | 0 |
| From Your Block to Our Block: How to Find Shared Structure Between Stochastic Block Models over Multiple Graphs.
|
AAAI | 2025 | 0 |
| Succinct Interaction-Aware Explanations.
|
KDD | 2025 | 0 |
| Discovering Sequential Patterns with Predictable Inter-event Delays.
|
AAAI | 2024 | 3 |
| Causal Discovery from Event Sequences by Local Cause-Effect Attribution.
|
NIPS/NeurIPS | 2024 | 6 |
| Identifying Confounding from Causal Mechanism Shifts.
|
AISTATS | 2024 | 5 |
| Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence.
|
ICML | 2024 | 7 |
| Learning Causal Networks from Episodic Data.
|
KDD | 2024 | 0 |
| What Are the Rules? Discovering Constraints from Data.
|
AAAI | 2024 | 2 |
| Finding Interpretable Class-Specific Patterns through Efficient Neural Search.
|
AAAI | 2024 | 0 |
| Data is Moody: Discovering Data Modification Rules from Process Event Logs.
|
ECML/PKDD | 2024 | 0 |
| Learning Causal Models under Independent Changes.
|
NIPS/NeurIPS | 2023 | 15 |
| Identifying Selection Bias from Observational Data.
|
AAAI | 2023 | 11 |
| Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition.
|
UAI | 2023 | 8 |
| Below the Surface: Summarizing Event Sequences with Generalized Sequential Patterns.
|
KDD | 2023 | 4 |
| Why Are We Waiting? Discovering Interpretable Models for Predicting Sojourn and Waiting Times.
|
SDM | 2023 | 0 |
| Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments.
|
AAAI | 2023 | 11 |
| Nothing but Regrets - Privacy-Preserving Federated Causal Discovery.
|
AISTATS | 2023 | 17 |
| Nonlinear Causal Discovery with Latent Confounders.
|
ICML | 2023 | 21 |
| Federated Learning from Small Datasets.
|
ICLR | 2023 | 0 |
| Discovering Significant Patterns under Sequential False Discovery Control.
|
KDD | 2022 | 8 |
| Discovering Invariant and Changing Mechanisms from Data.
|
KDD | 2022 | 6 |
| Inferring Cause and Effect in the Presence of Heteroscedastic Noise.
|
ICML | 2022 | 24 |
| Discovering Interpretable Data-to-Sequence Generators.
|
AAAI | 2022 | 3 |
| Naming the Most Anomalous Cluster in Hilbert Space for Structures with Attribute Information.
|
AAAI | 2022 | 2 |
| Differentially Describing Groups of Graphs.
|
AAAI | 2022 | 0 |
| Label-Descriptive Patterns and Their Application to Characterizing Classification Errors.
|
ICML | 2022 | 0 |
| Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent.
|
NIPS/NeurIPS | 2022 | 0 |
| Graph Similarity Description: How Are These Graphs Similar?
|
KDD | 2021 | 16 |
| What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules.
|
ICML | 2021 | 11 |
| Differentiable Pattern Set Mining.
|
KDD | 2021 | 9 |
| Mining Easily Understandable Models from Complex Event Logs.
|
SDM | 2021 | 8 |
| SUSAN: The Structural Similarity Random Walk Kernel.
|
SDM | 2021 | 15 |
| Discovering Fully Oriented Causal Networks.
|
AAAI | 2021 | 36 |
| Discovering Reliable Causal Rules.
|
SDM | 2021 | 0 |
| Discovering Functional Dependencies from Mixed-Type Data.
|
KDD | 2020 | 12 |
| Discovering Approximate Functional Dependencies using Smoothed Mutual Information.
|
KDD | 2020 | 13 |
| What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization.
|
WWW | 2020 | 50 |
| Explainable Data Decompositions.
|
AAAI | 2020 | 7 |
| Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity.
|
KDD | 2020 | 8 |
| The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery.
|
ICDM | 2020 | 5 |
| Just Wait For It... Mining Sequential Patterns with Reliable Prediction Delays.
|
ICDM | 2020 | 0 |
| Discovering Reliable Correlations in Categorical Data.
|
ICDM | 2019 | 4 |
| Discovering Robustly Connected Subgraphs with Simple Descriptions.
|
ICDM | 2019 | 6 |
| Sets of Robust Rules, and How to Find Them.
|
ECML/PKDD | 2019 | 18 |
| Identifiability of Cause and Effect using Regularized Regression.
|
KDD | 2019 | 33 |
| We Are Not Your Real Parents: Telling Causal from Confounded using MDL.
|
SDM | 2019 | 23 |
| Modern MDL meets Data Mining Insights, Theory, and Practice.
|
KDD | 2019 | 3 |
| Testing Conditional Independence on Discrete Data using Stochastic Complexity.
|
AISTATS | 2019 | 25 |
| Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms.
|
IJCAI | 2019 | 0 |
| Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms.
|
ICDM | 2018 | 13 |
| Causal Inference on Event Sequences.
|
SDM | 2018 | 15 |
| Accurate Causal Inference on Discrete Data.
|
ICDM | 2018 | 13 |
| Summarizing Graphs at Multiple Scales: New Trends.
|
ICDM | 2018 | 1 |
| Causal Inference on Multivariate and Mixed-Type Data.
|
ECML/PKDD | 2018 | 0 |
| Telling Cause from Effect Using MDL-Based Local and Global Regression.
|
ICDM | 2017 | 69 |
| Efficiently Discovering Unexpected Pattern-Co-Occurrences.
|
SDM | 2017 | 11 |
| MDL for Causal Inference on Discrete Data.
|
ICDM | 2017 | 39 |
| Discovering Reliable Approximate Functional Dependencies.
|
KDD | 2017 | 56 |
| Correlation by Compression.
|
SDM | 2017 | 1 |
| FACETS: Adaptive Local Exploration of Large Graphs.
|
SDM | 2017 | 25 |
| Efficiently Summarising Event Sequences with Rich Interleaving Patterns.
|
SDM | 2017 | 24 |
| Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery.
|
DMKD | 2017 | 40 |
| Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups.
|
ICDM | 2017 | 6 |
| Reconstructing an Epidemic Over Time.
|
KDD | 2016 | 61 |
| Universal Dependency Analysis.
|
SDM | 2016 | 0 |
| Flexibly Mining Better Subgroups.
|
SDM | 2016 | 0 |
| Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series.
|
SDM | 2016 | 0 |
| Causal Inference by Compression.
|
ICDM | 2016 | 0 |
| Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns.
|
KDD | 2016 | 0 |
| Causal Inference by Direction of Information.
|
SDM | 2015 | 28 |
| The Difference and the Norm - Characterising Similarities and Differences Between Databases.
|
ECML/PKDD | 2015 | 20 |
| Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics.
|
SDM | 2015 | 30 |
| Non-parametric Jensen-Shannon Divergence.
|
ECML/PKDD | 2015 | 26 |
| Getting to Know the Unknown Unknowns: Destructive-Noise Resistant Boolean Matrix Factorization.
|
SDM | 2015 | 20 |
| Erratum to: Unsupervised interaction-preserving discretization of multivariate data.
|
DMKD | 2015 | 0 |
| The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives.
|
MLJ | 2015 | 0 |
| A Fresh Look on Knowledge Bases: Distilling Named Events from News.
|
CIKM | 2014 | 62 |
| Narrow or Broad?: Estimating Subjective Specificity in Exploratory Search.
|
CIKM | 2014 | 27 |
| Multivariate Maximal Correlation Analysis.
|
ICML | 2014 | 44 |
| Uncovering the plot: detecting surprising coalitions of entities in multi-relational schemas.
|
DMKD | 2014 | 14 |
| Unsupervised interaction-preserving discretization of multivariate data.
|
DMKD | 2014 | 33 |
| VOG: Summarizing and Understanding Large Graphs.
|
SDM | 2014 | 0 |
| Mining Connection Pathways for Marked Nodes in Large Graphs.
|
SDM | 2013 | 45 |
| Detecting Bicliques in GF[q].
|
ECML/PKDD | 2013 | 3 |
| Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data.
|
ECML/PKDD | 2013 | 11 |
| Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data.
|
ICDM | 2013 | 13 |
| CMI: An Information-Theoretic Contrast Measure for Enhancing Subspace Cluster and Outlier Detection.
|
SDM | 2013 | 71 |
| Summarizing categorical data by clustering attributes.
|
DMKD | 2013 | 0 |
| Fast and reliable anomaly detection in categorical data.
|
CIKM | 2012 | 143 |
| Comparing apples and oranges: measuring differences between exploratory data mining results.
|
DMKD | 2012 | 25 |
| Spotting Culprits in Epidemics: How Many and Which Ones?
|
ICDM | 2012 | 244 |
| TourViz: interactive visualization of connection pathways in large graphs.
|
KDD | 2012 | 14 |
| The long and the short of it: summarising event sequences with serial episodes.
|
KDD | 2012 | 127 |
| Slim: Directly Mining Descriptive Patterns.
|
SDM | 2012 | 86 |
| Discovering Descriptive Tile Trees - By Mining Optimal Geometric Subtiles.
|
ECML/PKDD | 2012 | 13 |
| Model order selection for boolean matrix factorization.
|
KDD | 2011 | 88 |
| The Odd One Out: Identifying and Characterising Anomalies.
|
SDM | 2011 | 89 |
| MIME: A Framework for Interactive Visual Pattern Mining.
|
ECML/PKDD | 2011 | 62 |
| Comparing Apples and Oranges - Measuring Differences between Data Mining Results.
|
ECML/PKDD | 2011 | 11 |
| Krimp: mining itemsets that compress.
|
DMKD | 2011 | 326 |
| Maximum Entropy Modelling for Assessing Results on Real-Valued Data.
|
ICDM | 2011 | 20 |
| MIME: a framework for interactive visual pattern mining.
|
KDD | 2011 | 0 |
| Tell me what i need to know: succinctly summarizing data with itemsets.
|
KDD | 2011 | 0 |
| Summarising Data by Clustering Items.
|
ECML/PKDD | 2010 | 10 |
| Identifying the Components.
|
ECML/PKDD | 2009 | 39 |
| Low-Entropy Set Selection.
|
SDM | 2009 | 25 |
| Identifying the components.
|
DMKD | 2009 | 0 |
| Filling in the Blanks - Krimp Minimisation for Missing Data.
|
ICDM | 2008 | 36 |
| Finding Good Itemsets by Packing Data.
|
ICDM | 2008 | 45 |
| Preserving Privacy through Data Generation.
|
ICDM | 2007 | 41 |
| Characterising the difference.
|
KDD | 2007 | 67 |
| Item Sets that Compress.
|
SDM | 2006 | 176 |
| Compression Picks Item Sets That Matter.
|
ECML/PKDD | 2006 | 78 |