Publications


2019

[29] Q. Au, D. Schalk, G. Casalicchio, R. Schoedel, C. Stachl, and B. Bischl. “Component-Wise Boosting of Targets for Multi-Output Prediction” (2019). arXiv.

[28] A. Beer, D. Kazempour, M. Baur, and T. Seidl. “Human Learning in Data Science”. (Poster Extended Abstract). In Proceedings of the 21st International Conference of Human-Computer Interaction (HCII). Walt Disney World Swan and Dolphin Resort, Orlando, Florida, USA, July 26–31, 2019.

[27] A. Beer, D. Kazempour, and T. Seidl. “Rock - Let the points roam to their clusters themselves”. In Proceedings of the 22nd International Conference on ExtendingDatabase Technology (EDBT). Lisbon, Portugal, Mar. 26–29, 2019. pdf.

[26] A. Beer, D. Kazempour, L. Stephan, and T. Seidl. “LUCK - Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function” (short paper). In Proceedings of the 31st International Conference on Scientific and Statistical Database Management (SSDBM). Santa Cruz, California, USA, July 23–25, 2019. DOI.

[25] A. Beer, J. Lauterbach, and T. Seidl. “MORe++: k-Means Based Outlier Removal on High-Dimensional Data”. In Proceedings of the 12th International Conference on Similarity Search and Applications (SISAP). Newark, New York, USA, Oct. 2–4, 2019.

[24] A. Beer and T. Seidl. “Graph Ordering and Clustering - A Circular Approach”. In Proceedings of the 31st International Conference on Scientific and Statistical Database Management (SSDBM). Santa Cruz, California, USA, July 23–25, 2019. DOI.

[23] C. Böhm, M. Perdacher, and C. Plant. “A Novel Hilbert Curve for Cache-locality Preserving Loops”. In Proceedings of the IEEE International Conference on Big Data. Los Angeles, California, USA, Dec. 9–12, 2019.

[22] A. Bojchevski and S. Günnemann. “Adversarial Attacks on Node Embeddings”. In Proceedings of the 36th International Conference on Machine Learning (ICML). Long Beach Convention Center, Long Beach, California, USA, June 9–15, 2019. arXiv.

[21] F. Borutta, S. Schmoll, and S. Friedl. “Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning”. In Proceedings of the International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). Chicago, Illinois, USA, Nov. 5–8, 2019.

[20] E. Faerman, M. Rogalla, N. Strauß, A. Krüger, B. Blümel, M. Berrendorf, M. Fromm, and M.Schubert. “Spatial Interpolation with Message Passing Framework”. In Proceedings of the 1st IEEE ICDM Workshop on Deep Learning for Spatiotemporal Data, Algorithms, and Systems (DeepSpatial 2019). Beijing, China, Nov. 8–11, 2019.

[19] E. Faerman, O. Voggenreiter, F. Borutta, T. Emrich, M. Berrendorf, and M. Schubert.“Graph Alignment Networks with Node Matching Scores”. In Proceedings of the Workshops of the 33rd Conference on Neural Information Processing Systems (NeurIPS). Vancouver, Canada, Dec. 8–14, 2019.

[18] C. Fritz, M. Lebacher, and G. Kauermann. “Tempus Volat, Hora Fugit – A Survey of Dynamic Network Models in Discrete and Continuous Time”. The Computing Research Repository (CoRR) (2019). arXiv.

[17] M. Fromm, M. Berrendorf, E. Faerman, Y. Chen, B. Schüss, and M. Schubert. “XD-STOD: Cross-Domain Superresolution for Tiny Object Detection”. In Proceedings of the 1st IEEEICDM Workshop on Deep Learning for Spatiotemporal Data, Algorithms, and Systems (DeepSpatial 2019). Beijing, China, Nov. 8–11, 2019.

[16] J. Goschenhofer, F. M. J. Pfister, K. A. Yuksel, B. Bischl, U. Fietzek, and J. Thomas. “Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning” (2019). arXiv.

[15] D. Kazempour, A. Beer, and T. Seidl. “Data on RAILs: On interactive generation of artificial linear correlated data” (Poster Extended Abstract). In Proceedings of the 21st International Conference of Human-Computer Interaction (HCII). Walt Disney World Swan and Dolphin Resort, Orlando, Florida, USA, July 26–31, 2019.

[14] D. Kazempour, K. Emmerig, P. Kröger, and T. Seidl. “Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform”. In Proceedings of the 31st International Conference on Scientific and Statistical Database Management (SSDBM). Santa Cruz, California, USA, July 23–25, 2019. DOI.

[13] D. Kazempour, M. Kazakov, P. Kröger, and T. Seidl. “DICE: Density-based Interactive Clustering and Exploration”. In Proceedings of the 18th Symposium of Database Systems for Business, Technology and Web (BTW). Rostock, Germany, Mar. 4–8, 2019. pdf.

[12] D. Kazempour, L. Krombholz, P. Kröger, and T. Seidl. “A Galaxy of Correlations - Detecting Linear Correlated Clusters through k-Tuples Sampling using Parameter Space Transform”. In Proceedings of the 22nd International Conference on ExtendingDatabase Technology (EDBT). Lisbon, Portugal, Mar. 26–29, 2019. pdf.

[11] D. Kazempour and T. Seidl. “Insights into a running clockwork: On interactive process-aware clustering”. In Proceedings of the 22nd International Conference on ExtendingDatabase Technology (EDBT). Lisbon, Portugal, Mar. 26–29, 2019. pdf.

[10] D. Kazempour and T. Seidl. On Co-medians and Principal Component Analysis. In Proceedings of the 12th International Conference on Similarity Search and Applications(SISAP). Newark, New York, USA, Oct. 2–4, 2019.

[9] D. Kazempour and T. Seidl. “On systematic hyperparameter analysis through the example of subspace clustering”. In Proceedings of the 31st International Conference on Scientific and Statistical Database Management (SSDBM). Santa Cruz, California, USA, July 23–25, 2019. DOI.

[8] S. Klau, M.-L. Martin-Magniette, A.-L. Boulesteix, and S. Hoffmann. “Sampling uncertainty versus method uncertainty: a general framework with applications to omics biomarker selection”. Biometrical Journal (2019). DOI.

[7] M. Perdacher, C. Plant, and C. Böhm. “Cache-oblivious High-performance Similarity Join”. In Proceedings of the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD). Amsterdam, Netherlands, June 30–July 5, 2019. DOI.

[6] P. Probst, M. Wright, and A.-L. Boulesteix. “Hyperparameters and Tuning Strategies for Random Forest”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9.3 (2019). arxiv.

[5] C. A. Scholbeck, C. Molnar, C. Heumann, B. Bischl, and G. Casalicchio. “Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations” (2019). arxiv.

[4]        N. Schüller, A.-L. Boulesteix, B. Bischl, K. Unger, and R. Hornung. “Improved outcomeprediction across data sources through robust parameter tuning.” Tech. rep. 221. Institut für Statistik, LMU, 2019. pdf.

2018

[3]        M. Lotfollahi, F. A. Wolf, and F. J. Theis. “Generative Modeling and Latent Space Arithmetics Predict Single-Cell Perturbation Response across Cell Types, Studies and Species”. Preprint at bioRxiv (2018). DOI.

[2] F. Pfisterer, J. van Rijn, P. Probst, A. Müller, and B. Bischl. “Learning Multiple Defaults for Machine Learning Algorithms”. Accepted at Stat, preprint at arxiv (2018). arxiv.

[1] P. Probst, A. L. Boulesteix, and B. Bischl. “Tunability: Importance of Hyperparameters of Machine Learning Algorithms”. Journal of Machine Learning Research 20 (2018). arxiv.