2023 | |
Identifying Selection Bias from Observational Data. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2023. (19.6% acceptance rate) |
|
Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2023. (19.6% acceptance rate) |
|
Why Are We Waiting? Discovering Interpretable Models to Predict Waiting and Sojourn Times from Data. In: SIAM International Conference on Data Mining (SDM), SIAM, 2023. (27.4% acceptance rate) |
|
Mining Sequential Patterns with Gap Distributions. M.Sc. Thesis, Saarland University, 2023. |
|
2022 | |
Omen: Discovering Sequential Patterns with Reliable Prediction Delays. Knowledge and Information Systems vol.64(4), pp 1013-1045, Springer, 2022. (IF 2.822) |
|
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%) |
|
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. In: Proceedings of Neural Information Processing Systems (NeurIPS), PMLR, 2022. (25.7% acceptance rate) |
|
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) |
|
Plant'n'Seek: Can You Find the Winning Ticket?. In: Proceedings of the International Conference on Representation Learning (ICLR), OpenReview, 2022. |
|
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) |
|
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) |
|
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) |
|
Estimating Mutual Information via Geodesic kNN. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2022. |
|
Mining Interpretable Data-to-Sequence Generators. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2022. (15.0% acceptance rate) |
|
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) |
|
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. |
|
Regret-based Federated Causal Discovery. In: Proceedings of the ACM SIGKDD Workshop on Causal Discovery, PMLR, 2022. |
|
Causal Inference with Heteroscedastic Noise Models. In: Proceedings of the AAAI Workshop on Information Theoretic Causal Inference and Discovery (ITCI'22), 2022. |
|
More than the Sum of its Parts - Pattern Mining, Neural Networks, and How They Complement Each Other. Dissertation, Saarland University, 2022. |
|
Subgroup Discovery for Structured Target Concepts. Dissertation, Saarland University, 2022. |
|
Disentangling Neural Networks with Block Diagonal Coefficient. M.Sc. Thesis, Saarland University, 2022. |
|
Best Enjoyed Together: Mining Dependent Patterns. M.Sc. Thesis, Saarland University, 2022. |
|
Benchmarking XAI Methods using Causal Graphs. M.Sc. Thesis, Saarland University, 2022. |
|
2021 | |
Data-driven Equation for Drug-Membrane Permeability across Drugs and Membranes. Journal of Chemical Physics vol.24(154), AIP, 2021. (IF 2.991) |
|
Integrative Analysis of Epigenetics Data Identifies Gene-Specific Regulatory Elements. Nucleic Acids Research, Oxford University Press, 2021. (IF 16.97) |
|
Discovering Reliable Causal Rules. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate) |
|
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) |
|
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) |
|
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) |
|
SUSAN: The Structural Similarity Random Walk Kernel. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate) |
|
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) |
|
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) |
|
Discovering Fully Oriented Causal Networks. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2021. (21.3% acceptance) |
|
Relative Flatness and Generalization. In: Proceedings of Advances in Neural Information Processing Systems (NeurIPS), PMLR, 2021. (26.2% acceptance rate) |
|
Mining Easily Understandable Models from Complex Event Data. In: SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate) |
|
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. |
|
Stable, Diverse, and Efficient Markov Blanket Discovery. M.Sc. Thesis, Saarland University, 2021. |
|
Discovering Robust Rules that Express Positive and Negative Associations in Data. M.Sc. Thesis, Saarland University, 2021. |
|
Where are my Parents? Causal Discovery Under a Weaker Faithfulness Assumption. M.Sc. Thesis, Saarland University, 2021. |
|
Mining Memorable Motifs from Music. M.Sc. Thesis, Saarland University, 2021. |
|
Causal Inference from Different Contexts using Algorithmic Causal Models. M.Sc. Thesis, Saarland University, 2021. |
|
Density Preserving t-SNE. B.Sc. Thesis, Saarland University, 2021. |
|
GPU-accelerated Branch-and-Bound with Applications in High-Dimensional Markov Blanket Discovery. B.Sc. Thesis, Saarland University, 2021. |
|
2020 | |
Discovering Dependencies with Reliable Mutual Information. Knowledge and Information Systems vol.62, pp 4223-4253, Springer, 2020. (IF 2.936) |
|
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) |
|
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%) |
|
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) |
|
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) |
|
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%) |
|
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) |
|
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) |
|
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) |
|
Towards Plausible Graph Anonymization. In: Proceedings of the Network and Distributed System Security Symposium (NDSS), The Internet Society, 2020. (17.4% acceptance rate) |
|
Data-driven equation for drug-membrane permeability across drugs and membranes. Technical Report 2012.01766, arXiv, 2020. |
|
A Weaker Faithfulness Assumption based on Triple Interactions. Technical Report 2010.14265, arXiv, 2020. |
|
Factoring out prior knowledge from low-dimensional embeddings. M.Sc. Thesis, Saarland University, 2020. |
|
What's in the Box? Explaining Neural Networks with Robust Rules. M.Sc. Thesis, Saarland University, 2020. |
|
SPRAP: Detecting Opinion Spam Campaigns in Online Rating Services. M.Sc. Thesis, Saarland University, 2020. |
|
2019 | |
Telling Cause from Effect by Local and Global Regression. Knowledge and Information Systems vol.60(3), pp 1277-1305, IEEE, 2019. (IF 2.397) |
|
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) |
|
Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), IEEE, 2019. (18.5% acceptance rate) |
|
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) |
|
Discovering Reliable Correlations in Categorical Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'19), IEEE, 2019. (18.5% acceptance rate) |
|
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) |
|
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%) |
|
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) |
|
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) |
|
Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the ACM SIGKDD Workshop on Mining and Learning from Graphs (MLG), 2019. |
|
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. |
|
Summarizing Dynamic Graphs using MDL. In: Proceedings of the ECMLPKDD Workshop on Graph Embedding and Mining (GEM), 2019. (oral presentation, 21% acceptance rate) |
|
Proceedings of the ACM SIGKDD Workshop on Learning and Mining for Cybersecurity (LEMINCS). , 2019. |
|
How to be Grim: Explaining Data at Different Granularity Levels. M.Sc. Thesis, Saarland University, 2019. |
|
How to Make Cake: Finding Causal Patterns for Marked Events in Sequences. M.Sc. Thesis, Saarland University, 2019. |
|
Causal Discovery using MDL-based Regression. M.Sc. Thesis, Saarland University, 2019. |
|
Summarizing Dynamic Graphs using MDL. M.Sc. Thesis, Saarland University, 2019. |
|
2018 | |
Origo: Causal Inference by Compression. Knowledge and Information Systems vol.56(2), pp 285-307, Springer, 2018. (IF 2.247) |
|
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) |
|
Generating Realistic Synthetic Population Datasets. Transactions on Knowledge Discovery from Data vol.12(4), pp 1-45, ACM, 2018. (IF 1.68) |
|
Accurate Causal Inference on Discrete Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'18), IEEE, 2018. (19.9% acceptance rate) |
|
Causal Inference on Event Sequences. In: Proceedings of the SIAM Conference on Data Mining (SDM), pp 55-63, SIAM, 2018. (23.2% acceptance rate) |
|
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) |
|
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) |
|
Rule Discovery for Exploratory Causal Reasoning. In: Proceedings of the NeurIPS 2018 workshop on Causal Learning, pp 1-14, 2018. |
|
Stochastic Complexity for Testing Conditional Independence on Discrete Data. In: Proceedings of the NeurIPS 2018 workshop on Causal Learning, pp 1-12, 2018. |
|
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. |
|
Efficiently Summarising Data with Patterns that Overlap. M.Sc. Thesis, Saarland University, 2018. |
|
OctOPUS: Branch-and-Bound Search with both Sibling Propagations and Closure Operators. M.Sc. Thesis, Saarland University, 2018. |
|
Smoothie: Smoothing Discrete Data. M.Sc. Thesis, Saarland University, 2018. |
|
Reverse-Engineering Epidemics in Large Weighted Graphs. M.Sc. Thesis, Saarland University, 2018. |
|
More Robust Interaction Preserving Discretization. M.Sc. Thesis, Saarland University, 2018. |
|
Reconstructing Dependency Networks by Cumulate Entropy Estimation. M.Sc. Thesis, Saarland University, 2018. |
|
2017 | |
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) |
|
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) |
|
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) |
|
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) |
|
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%) |
|
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) |
|
Correlation by Compression. In: Proceedings of the SIAM Conference on Data Mining (SDM), SIAM, 2017. (25% acceptance rate) |
|
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%) |
|
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%) |
|
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) |
|
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) |
|
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. |
|
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. |
|
Efficiently Summarising Event Sequences with Rich Interleaving Patterns. Technical Report 1701.08096, arXiv, 2017. |
|
Automated Ontology Refinement using Compression-based Learning. M.Sc. Thesis, Technische Universität Darmstadt, 2017. |
|
Explore: Discovering Power-Law Communities in Large Graphs. M.Sc. Thesis, Saarland University, 2017. |
|
Compress it with fire: adaptive codes for MDL-based pattern mining. M.Sc. Thesis, Saarland University, 2017. |
|
Finding Difference and Norm between Sequence Databases. B.Sc. Thesis, Saarland University, 2017. |
|
2016 | |
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) |
|
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%) |
|
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) |
|
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) |
|
Universal Dependency Analysis. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 792-800, SIAM, 2016. (overall 25% acceptance rate) |
|
Flexibly Mining Better Subgroups. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 585-593, SIAM, 2016. (overall 25% acceptance rate) |
|
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) |
|
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) |
|
Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data (ECMLPKDD). Springer, 2016. (Part I) |
|
Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data (ECMLPKDD). Springer, 2016. (Part II) |
|
Fast Computation of Highest Correlated Segments in Multivariate Time-Series. M.Sc. Thesis, Saarland University, 2016. |
|
Squish: Efficiently Summarising Sequences with Rich and Interleaving Patterns. M.Sc. Thesis, Saarland University, 2016. |
|
Spaghetti: Finding Storylines in Large Collections of Documents. M.Sc. Thesis, Saarland University, 2016. |
|
An Approach for Ontological Pattern-based Summarization. M.Sc. Thesis, Saarland University, 2016. |
|
Towards Summarising Large Transaction Databases. M.Sc. Thesis, Saarland University, 2016. |
|
Summarising and Recommending with Skipisodes. M.Sc. Thesis, Saarland University, 2016. |
|
Skim: Alternative Candidate Selections for Slim through Sketching. B.Sc. Thesis, Saarland University, 2016. |
|
2015 | |
Summarizing and Understanding Large Graphs. Statistical Analysis and Data Mining vol.8(3), pp 183-202, Wiley, 2015. |
|
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) |
|
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. |
|
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. |
|
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. |
|
AdaptiveNav: Adaptive Discovery of Interesting and Surprising Nodes in Large Graphs. In: Proceedings of the IEEE Conference on Visualization (VIS), IEEE, 2015. |
|
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. |
|
Causal Inference by Direction of Information. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 909-917, SIAM, 2015. |
|
Correlation by Compression. M.Sc. Thesis, Saarland University, 2015. |
|
Information-Theoretic Supervised Feature Selection for Continuous Data. M.Sc. Thesis, Saarland University, 2015. |
|
2014 | |
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) |
|
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) |
|
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) |
|
Efficient Discovery of the Most Interesting Associations. Transactions on Knowledge Discovery from Data vol.8(3), pp 1-31, ACM, 2014. (IF 1.68) |
|
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) |
|
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) |
|
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) |
|
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) |
|
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) |
|
Interesting Patterns. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 105-134, pp 105-134, Springer, 2014. |
|
Frequent Pattern Mining Algorithms for Data Clustering. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 403-424, pp 403-424, Springer, 2014. |
|
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. |
|
Supporting Exploratory Search Through User Modeling. In: Proceedings of the UMAP Joint Workshop on Personalized Information Access (PIA), pp 1-6, 2014. |
|
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. |
|
Slimmer, outsmarting Slim. PhD Poster and Video at: the 13th International Symposium on Intelligent Data Analysis (IDA), Springer, 2014. |
|
Causal Inference by Packing Data. B.Sc. Thesis, Saarland University, 2014. |
|
2013 | |
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%) |
|
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) |
|
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. |
|
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%) |
|
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. |
|