Publications


2021

[135] M. Becker, S. Gruber, J. Richter, J. Moosbauer, and B. Bischl. “mlr3hyperband: Hyper-band for ’mlr3’". 2021. MLR. GitHub.
[134] M. Becker, M. Lang, J. Richter, B. Bischl, and D. Schalk. “mlr3tuning: Tuning for ’mlr3’". 2021. MLR. GitHub.
[133] M. Becker, J. Richter, M. Lang, B. Bischl, and M. Binder. “bbotk: Black-Box Optimization Toolkit". 2021. MLR. GitHub.
[132] A. Beer, E. Allerborn, V. Hartmann, and T. Seidl. “KISS - A fast kNN-based Importance Score for Subspaces". In Proceedings of the 24th International Conference onExtending Database Technology (EDBT 2021). Nicosia, Cyprus, Mar. 23–26, 2021.
[131] Y. Elazar, N. Kassner, S. Ravfogel, A. Ravichander, E. Hovy, H. Schütze, and Y. Goldberg. “Measuring and Improving Consistency in Pretrained Language Models". preprint at arXiv (2021). arXiv.
[130] C. Fritz, E. Dorigatti, and D. Rügamer. “Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany". preprint at arXiv (2021). arXiv.
[129] N. Kassner, P. Dufter, and H. Schütze. “Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models". In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021). Virtual, Apr. 19–23, 2021. arXiv.
[128] G. König, C. Molnar, B. Bischl, and M. Grosse-Wentrup. “Relative Feature Importance". In Proceedings of the 25th International Conference on Pattern Recognition (ICPR 2020). Virtual - Milano, Italy, Jan. 10–15, 2021. arXiv.
[127] M. Lang. “mlr3measures: Performance Measures for ’mlr3’". 2021. R package version 0.3.1.
[126] M. Lang, B. Bischl, J. Richter, X. Sun, and M. Binder. “paradox: Define and Work with Parameter Spaces for Complex Algorithms". 2021. MLR. GitHub.
[125] S. Obermeier, A. Beer, F. Wahl, and T. Seidl. “Cluster Flow — an Advanced Concept for Ensemble-Enabling, Interactive Clustering". In Proceedings of the 19th Symposium of Database Systems for Business, Technology and Web (BTW 2021). Dresden, Germany, Sept. 13–17, 2021.
[124] S. Schmoll and M. Schubert. “Semi-Markov Reinforcement Learning for Stochastic Resource Collection". In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2021). Yokohama, Japan (postponed due to the Corona pandemic), Jan. 2021. DOI.
[123] P. Schratz and M. Becker. “mlr3spatiotempcv: Spatiotemporal Resampling Methods for’mlr3’". 2021. R package version 0.1.1.
[122] M. Weigert, A. Bauer, J. Gernert, M. Karl, A. Nalmpatian, H. Küchenhoff, and J.Schmude. “Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on traveldistances". Tourism Economics (2021). DOI.



2020

[121] A. Agrawal, F. Pfisterer, B. Bischl, J. Chen, S. Sood, S. Shah, F. Buet-Golfouse, B. A.Mateen, and S. Vollmer. “Debiasing classifiers: is reality at variance with expectation?". preprint at arXiv (2020). arXiv.
[120] M. C. Altinigneli, L. Miklautz, C. Böhm, and C. Plant. “Hierarchical Quick Shift Guided Recurrent Clustering". In Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE 2020). Dallas, TX, USA, Apr. 20–24, 2020. URL.
[119] P. F. M. Baumann, T. Hothorn, and D. Rügamer. “Deep Conditional Transformation Models". preprint at arXiv (2020). arXiv.
[118] M. Becker, P. Schratz, M. Lang, and B. Bischl. “mlr3fselect: Feature Selection for ’mlr3’". 2020. R package version 0.4.1.
[117] A. Beer, V. Hartmann, and T. Seidl. “Orderings of Data - more than a Tripping Hazard". In Proceedings of the 32nd International Conference on Scientific and Statistical Database Management (SSDBM 2020). Vienna, Austria, July 7–9, 2020. URL.
[116] A. Beer, D. Kazempour, J. Busch, A. Tekles, and T. Seidl. “Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data". In Proceedings of the Conference on Lernen. Wissen. Daten. Analysen (LWDA 2020). Bonn, Germany, Sept. 9–11, 2020.
[115] A. Beer, D. Seeholzer, N. S. Schüler, and T. Seidl. “Angle-Based Clustering". In Proceedings of the 13th International Conference on Similarity Search and Applications (SISAP 2020). Virtual, Sept. 30–Oct. 2, 202. arXiv.
[114] A. Bender, D. Rügamer, F. Scheipl, and B. Bischl. “A General Machine Learning Framework for Survival Analysis". In Proceedings of the European Conference on Machine Learning, Principles, and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020). Ghent, Belgium, Sept. 14–18, 2020. arXiv.
[113] M. Berrendorf, E. Faerman, V. Melnychuk, V. Tresp, and T. Seidl. “Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned". In Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020). Virtual, Apr. 14–17, 2020. arXiv. URL.
[112] M. Berrendorf, E. Faerman, L. Vermue, and V. Tresp. “Interpretable and Fair Comparisonof Link Prediction or Entity Alignment Methods". In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020). Virtual, Dec. 14–17, 2020. arXiv.
[111] A. Beyer, G. Kauermann, and H. Schütze. “Embedding Space Correlation as a Measure of Domain Similarity". In Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020). Marseille, France, May 11–16, 2020.
[110] M. Binder, J. Moosbauer, J. Thomas, and B. Bischl. “Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection". preprint at arXiv (2020). arXiv.
[109] M. Binder, J. Moosbauer, J. Thomas, and B. Bischl. “Multi-Objective Hyperparameter Tuning and Feature Selection Using Filter Ensembles". In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020). Cancun, Mexico, July 8–12, 2020. DOI.
[108] M. Binder, F. Pfisterer, L. Schneider, B. Bischl, M. Lang, and S. Dandl. “mlr3pipelines: Preprocessing Operators and Pipelines for ’mlr3’.". 2020. MLR. GitHub.
[107] C. Böhm and C. Plant. “Massively Parallel Graph Drawing and Representation Learning". In Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2020). Virtual, Dec. 10–13, 2020.
[106] C. Böhm and C. Plant. “Massively Parallel Random Number Generation". In Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2020). Virtual, Dec. 10–13, 2020.
[105] F. Borutta, D. Kazempour, F. Marty, P. Kröger, and T. Seidl. “Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform". In Proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020). Singapore, May 11–14, 2020.
[104] A.-L. Boulesteix, A. Charlton, S. Hoffmann, and H. Seibold. “A replication crisis in methodological research?". Significance 17.5 (2020). DOI.
[103] G. Brasó and L. Leal-Taixé. “Learning a Neural Solver for Multiple Object Tracking". In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, June 14–19, 2020. arXiv.
[102] J. Busch, E. Faerman, M. Schubert, and T. Seidl.. “Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering". In Proceedings of the Workshop at the 34rd Conference on Neural Information Processing Systems (NeurIPS 2020): Self-Supervised Learning - Theory and Practice (SSL 2020). Virtual, Dec. 12, 2020. arXiv.
[101] S. Dandl, C. Molnar, M. Binder, and B. Bischl. “Multi-Objective Counterfactual Explanations". In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN 2020). Leiden, Netherlands, Sept. 5–9, 2020. DOI.
[100] D. Davletshina, V. Melnychuk, V. Tran, H. Singla, M. Berrendorf, E. Faerman, M. Fromm, and M. Schubert. “Unsupervised Anomaly Detection for X-Ray Images". The Computing Research Repository (CoRR) (2020). arXiv.
[99] N. Ellenbach, A.-L. Boulesteix, B. Bischl, K. Unger, and R. Hornung. “Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning". Journal of Classification (2020). DOI.
[98] E. Faerman, F. Borutta, J. Busch, and M. Schubert. “Ada-LLD: Adaptive Node Similarity Using Multi-Scale Local Label Distributions". In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020). Virtual, Dec. 14–17, 2020.
[97] M. Herrmann, P. Probst, R. Hornung, V. Jurinovic, and A.-L. Boulesteix. “Large-scale benchmark study of survival prediction methods using multi-omics data". Briefings in Bioinformatics (2020). DOI.
[96] M. Herrmann and F. Scheipl. “Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction". preprint at arXiv (2020). arXiv.
[95] J. Jungmaier, N. Kassner, and B. Roth. “Dirichlet-Smoothed Word Embeddings for Low-Resource Settings". In Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020). Marseille, France, May 11–16, 2020. pdf.
[94] N. Kassner, B. Krojer, and H. Schütze. “Are Pretrained Language Models Symbolic Reasoners over Knowledge?". In Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL 2020). Virtual, Nov. 19–20, 2020. DOI.
[93] N. Kassner and H. Schütze. “BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA". In Findings of the Association for Computational Linguistics (EMNLP 2020). Virtual, Nov. 2020. DOI.
[92] N. Kassner and H. Schütze. “Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly". In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020). Virtual, July 5–10, 2020. pdf.
[91] D. Kazempour, A. Beer, P. Kröger, and T. Seidl. “I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering through piecewise-linear approximations of manifolds". In Proceedings of the 8th Workshop on High Dimensional Data Mining (HDM 2020) at the 20th IEEE International Conference on Data Mining (ICDM 2020). Virtual, Nov. 17–20, 2020.
[90] D. Kazempour, P. Kröger, and T. Seidl. “Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering". In Proceedings of the 8th Workshop on High Dimensional Data Mining (HDM 2020) at the 20th IEEE International Conference on Data Mining (ICDM 2020). Virtual, Nov. 17–20, 2020.
[89] D. Kazempour, L. M. Yan, P. Kröger, and T. Seidl. “You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters". In Proceedings of the 8th Workshop on High Dimensional Data Mining (HDM 2020) at the 20th IEEE International Conference on Data Mining (ICDM 2020). Virtual, Nov. 17–20, 2020.
[88] S. Klau, S. Hoffmann, C. Patel, J. P. A. Ioannidis, and A.-L. Boulesteix. “Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework". International Journal of Epidemiology (2020).
[87] 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 62.3 (2020). DOI.
[86] P. Kopper, S. Pölsterl, C. Wachinger, B. Bischl, A. Bender, and D. Rügamer. “Semi-Structured Deep Piecewise Exponential Models". preprint at arXiv (2020). arXiv.
[85] M. Lang. “mlr3db: Data Base Backend for ’mlr3’". 2020. MLR. GitHub.
[84] M. Lang. “mlr3oml: Connector Between ’mlr3’ and ’OpenML'". 2020. MLR. GitHub.
[83] M. Lang, Q. Au, S. Coors, and P. Schratz. “mlr3learners: Recommended Learners for ’mlr3’". 2020. MLR. GitHub.
[82] M. Lang, P. Schratz, and R. Sonabend. “mlr3viz: Visualizations for ’mlr3’". 2020. MLR. GitHub.
[81] M. Lange, V. Bergen, M. Klein, M. Setty, B. Reuter, M. Bakhti, H. Lickert, M. Ansari,J. Schniering, H. B. Schiller, D. Pe’er, and F. J. Theis. “CellRank for directed single-cellfate mapping". preprint at bioRxiv (2020). DOI. pdf.
[80] A. Maldonado, J. Sontheim, F. Richter, and T. Seidl. “Performance Skyline: Inferring Process Performance Models from Interval Events". In Proceedings of the 1st InternationalWorkshop on Streaming Analytics for Process Mining (SA4PM 2020) in conjunction with the 2nd International Conference on Process Mining (ICPM 2020). Virtual, Oct. 4–9, 2020.
[79] A. Markham and M. Grosse-Wentrup. “Measurement Dependence Inducing Latent Causal Models". In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 2020). Toronto, Canada, Aug. 3–6, 2020.
[78] L. Miklautz, D. Mautz, C. Altinigneli, C. Böhm, and C. Plant. “Deep embedded non-redundant clustering". In Proceedings of the 34th Conference on Artificial Intelligence (AAAI 2020). New York City, New York, USA, Feb. 7–12, 2020.
[77] C. Molnar, G. König, B. Bischl, and G. Casalicchio. “Model-agnostic Feature Importance and Effects with Dependent Features–A Conditional Subgroup Approach". preprint at arXiv (2020). arXiv.
[76] C. Molnar, G. König, J. Herbinger, T. Freiesleben, S. Dandl, C. A. Scholbeck, G. Casalicchio, M. Grosse-Wentrup, and B. Bischl. “Pitfalls to Avoid when Interpreting Machine Learning Models". In Proceedings of the Workshop XXAI of the 37th International Conference on Machine Learning (ICML 2020). Virtual, July 12–18, 2020. arXiv.
[75] S. Obermeier, M. Berrendorf, and P. Kröger. “Memory-Efficient RkNN Retrieval by Non-linear k-Distance Approximation". In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020). Virtual, Dec. 14–17, 2020.
[74] M. Perdacher, C. Plant, and C. Böhm. “Improved Data Locality Using Morton-orderCurve on the Example of LU Decomposition". In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020). Virtual, Dec. 10–13, 2020.
[73] C. Plant, S. Biedermann, and C. Böhm. “Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning". In Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2020). Virtual, Aug. 23–27, 2020.
[72] D. Pulatov and M. Lang. “mlr3cluster: Cluster Extension for ’mlr3’". 2020. MLR. GitHub.
[71] D. Rügamer, C. Kolb, and N. Klein. “A Unified Network Architecture for Semi-StructuredDeep Distributional Regression". preprint at arXiv (2020). arXiv.
[70] D. Rügamer, F. Pfisterer, and B. Bischl. “Neural Mixture Distributional Regression". preprint at arXiv (2020). arXiv.
[69] M. J. Sabet, P. Dufter, and H. Schütze. “SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings". preprint at arXiv (2020). arXiv.
[68] M. Schneble and G. Kauermann. “Intensity Estimation on Geometric Networks with Penalized Splines". preprint at arXiv (2020). arXiv.
[67] P. Schratz, M. Lang, B. Bischl, and M. Binder. “mlr3filters: Filter Based Feature Selectionfor ’mlr3’". 2020. MLR. GitHub.
[66] H. Seibold, A. Charlton, A.-L. Boulesteix, and S. Hoffmann. “Statisticians roll up yoursleeves! There’s a crisis to be solved". preprint at MetaArXiv (2020). DOI.
[65] O. Shchur, M. Biloš, and S. Günnemann. “Intensity-Free Learning of Temporal Point Processes (selected for spotlight presentation)". In Proceedings of the 8th International Conference on Learning Representations (ICLR 2020). Virtual, Apr. 26–May 1, 2020. arXiv.
[64] O. Shchur, N. Gao, M. Biloš, and S. Günnemann. “Fast and Flexible Temporal PointProcesses with Triangular Maps". In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, Apr. Dec. 6–12, 2020.
[63] R. Sonabend, F. Kiraly, and M. Lang. “mlr3proba: Probabilistic Supervised Learning for’mlr3’". 2020. R package version 0.2.6. pdf.
[62] R. Sonabend, F. J. Kyráli, A. Bender, B. Bischl, and M. Lang. “mlr3proba: MachineLearning Survival Analysis in R". preprint at arXiv (2020). arXiv.
[61] C. Stachl, Q. Au, R. Schoedel, S. D. Gosling, G. M. Harari, D. Buschek, S. T. Völkel, T. Schuwerk, M. Oldemeier, T. Ullmann, H. Hussmann, B. Bischl, and M. Bühner. “Predicting personality from patterns of behavior collected with smartphones". Proceedings of the National Academy of Sciences 117.30 (2020). DOI.
[60] D. Zügner and S. Günnemann. “Certifiable Robustness of Graph Convolutional Networks under Structure Perturbation". In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020). San Diego, California, USA, Aug. 23–27, 2020.



2019

[59] Q. Au, D. Schalk, G. Casalicchio, R. Schoedel, C. Stachl, and B. Bischl. “Component-Wise Boosting of Targets for Multi-Output Prediction". preprint at arXiv (2019). arXiv.
[58] 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 2019). Walt Disney World Swan and Dolphin Resort, Orlando, Florida, USA, July 26–31, 2019.
[57] A. Beer, D. Kazempour, and T. Seidl. “Rock - Let the points roam to their clusters themselves". In Proceedings of the 22nd International Conference on Extending Database Technology (EDBT 2019). Lisbon, Portugal, Mar. 26–29, 2019. pdf.
[56] 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 2019). Santa Cruz, California, USA, July 23–25, 2019. DOI.
[55] 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 2019). Newark, New York, USA, Oct. 2–4, 2019.
[54] A. Beer, N. S. Schüler, and T. Seidl. “A Generator for Subspace Clusters". In Proceedings of the Conference on Lernen. Wissen. Daten. Analysen (LWDA 2019). Berlin, Germany, Sept. 30–Oct. 2, 2019. pdf.
[53] 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 2019). Santa Cruz, California, USA, July 23–25, 2019. DOI.
[52] L. Beggel, M. Pfeiffer, and B. Bischl. “Robust Anomaly Detection in Images Using Adversarial Autoencoders". In Proceedings of the European Conference on Machine Learning, Principles, and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, Sept. 16–20, 2019. URL.
[51] V. Bergen, M. Lange, S. Peidli, F. A. Wolf, and F. J. Theis. “Generalizing RNA velocity to transient cell states through dynamical modeling". preprint at bioRxiv (2019). DOI.
[50] M. Berrendorf, F. Borutta, and P. Kröger. “k-Distance Approximation for Memory-Efficient RkNN Retrieval". In Proceedings of the 12th International Conference on Similarity Search and Applications (SISAP 2019). Newark, New York, USA, Oct. 2–4, 2019.
[49] M. Berrendorf, E. Faerman, L. Vermue, and V. Tresp. “Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank". preprint at arXiv (2019). arXiv.
[48] M. Biloš, B. Charpentier, and S. Günnemann. “Uncertainty on Asynchronous Time Event Prediction (Poster)". In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, Dec. 8–14, 2019. arXiv.
[47] M. Binder, S. Dandl, and J. Moosbauer. “mosmafs: Multi-Objective Simultaneous Model and Feature Selection". R package (2019).
[46] 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 (IEEE BigData 2019). Los Angeles, California, USA, Dec. 9–12, 2019.
[45] A. Bojchevski and S. Günnemann. “Adversarial Attacks on Node Embeddings via Graph Poisoning". In Proceedings of the 36th International Conference on Machine Learning (ICML 2019). Long Beach Convention Center, Long Beach, California, USA, June 9–15, 2019. arXiv.
[44] A. Bojchevski and S. Günnemann. “Certifiable Robustness to Graph Perturbations". In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, Dec. 8–14, 2019. arXiv.
[43] F. Borutta, J. Busch, E. Faerman, A. Klink, and M. Schubert. “Structural Graph Representations based on Multiscale Local Network Topologies". In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI 2019). Thessaloniki, Greece, Oct. 14–17, 2019.
[42] F. Borutta, P. Kröger, and T. Hubauer. “A Generic Summary Structure for Arbitrarily Oriented Subspace Clustering in Data Streams". In Proceedings of the 12th International Conference on Similarity Search and Applications (SISAP 2019). Newark, New York, USA, Oct. 2–4, 2019.
[41] 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 2019). Chicago, Illinois, USA, Nov. 5–8, 2019.
[40] L. Della Libera, V. Golkov, Y. Zhu, A. Mielke, and D. Cremers. “Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods". preprint at arXiv (2019). arXiv.
[39] 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 Workshop on Deep Learning for Spatiotemporal Data, Algorithms, and Systems (DeepSpatial 2019) at the 19th IEEE International Conference on Data Mining (ICDM 2019). Beijing, China, Nov. 8–11, 2019.
[38] 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 2019). Vancouver, Canada, Dec. 8–14, 2019.
[37] 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.
[36] 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 Workshop on Deep Learning for Spatiotemporal Data, Algorithms, and Systems (DeepSpatial 2019) at the 19th IEEE International Conference on Data Mining (ICDM 2019). Beijing, China, Nov. 8–11, 2019.
[35] J. Goldsmith, F. Scheipl, L. Huang, J. Wrobel, C. Di, J. Gellar, J. Harezlak, M. W. McLean, B. Swihart, L. Xiao, C. Crainiceanu, and P. T. Reiss. “refund: Regression with Functional Data". R package version 0.1-21 (2019). URL.
[34] 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". In Proceedings of the European Conference on Machine Learning, Principles, and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, Sept. 16–20, 2019. arXiv. URL.
[33] J. Held, A. Beer, and T. Seidl. “Chain-detection Between Clusters". Datenbank-Spektrum 19 (2019).
[32] J. Held, A. Beer, and T. Seidl. “Chain-detection for DBSCAN (Poster Extended Abstract)". In Proceedings of the 18th Symposium of Database Systems for Business, Technology and Web (BTW 2019). Rostock, Germany, Mar. 4–8, 2019.
[31] D. Kazempour, A. Beer, O. Schrüfer, and T. Seidl. “Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents". In Proceedings of the Conference on Lernen. Wissen. Daten. Analysen (LWDA 2019). Berlin, Germany, Sept. 30–Oct. 2, 2019. pdf.
[30] 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 2019). Walt Disney World Swan and Dolphin Resort, Orlando, Florida, USA, July 26–31, 2019.
[29] 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 2019). Santa Cruz, California, USA, July 23–25, 2019. DOI.
[28] D. Kazempour, M. Hünemörder, and T. Seidl. “On coMADs and Principal Component Analysis". In Proceedings of the 12th International Conference on Similarity Search and Applications (SISAP 2019). Newark, New York, USA, Oct. 2–4, 2019.
[27] 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 2019). Rostock, Germany, Mar. 4–8, 2019. pdf.
[26] 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 Extending Database Technology (EDBT 2019). Lisbon, Portugal, Mar. 26–29, 2019. pdf.
[25] D. Kazempour and T. Seidl. “Insights into a running clockwork: On interactive process-aware clustering". In Proceedings of the 22nd International Conference on Extending Database Technology (EDBT 2019). Lisbon, Portugal, Mar. 26–29, 2019. pdf.
[24] 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 2019). Newark, New York, USA, Oct. 2–4, 2019.
[23] 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 2019). Santa Cruz, California, USA, July 23–25, 2019. DOI.
[22] D. Kazempour, L. M. Yan, and T. Seidl. “From Covariance to Comode in context of Principal Component Analysis". In Proceedings of the Conference on Lernen. Wissen. Daten. Analysen (LWDA 2019). Berlin, Germany, Sept. 30–Oct. 2, 2019.
[21] J. Klicpera, S. Weißenberger, and S. Günnemann. “Diffusion Improves Graph Learning". In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, Dec. 8–14, 2019. arXiv.
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2018

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