Publications


2021

[80]        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). Yokohama, Japan (postponed due to the Corona pandemic), Jan. 2021. DOI.


2020

[79] A. Beer, V. Hartmann, and T. Seidl. “Orderings of Data - more than a Tripping Hazard”. In Proceedings of the 32nd Intrnational Conference on Scientific and Statistical DatabaseManagement (SSDBM). Vienna, Austria, July 7–9, 2020. URL.

[78] 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). online, Apr. 14–17, 2020. arXiv. URL.

[77] 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). Marseille, France, May 11–16, 2020.

[76] M. Binder, J. Moosbauer, J. Thomas, and B. Bischl. “Multi-Objective HyperparameterTuning and Feature Selection Using Filter Ensembles”. In Proceedings of the 2020 Geneticand Evolutionary Computation Conference (GECCO). Cancun, Mexico, July 8–12, 2020. DOI.

[75] 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). Singapore, May 11–14, 2020.

[74] 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). Virtual, June 14–19, 2020. arXiv

[73] 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

[72] N. Ellenbach, A.-L. Boulesteix, B. Bischl, K. Unger, and R. Hornung. “Improved outcomeprediction across data sources through robust parameter tuning”. Journal of Classification (2019). DOI.

[71] 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). arXiv.

[70] 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). Marseille, France, May 11–16, 2020. pdf).

[69] N. Kassner, B. Kroje, and H. Schütze. “Pre-trained Language Models as Symbolic Reasoners over Knowledge?". preprint at aRxiv (2020). arXiv.

[68] N. Kassner and H. Schütze. “BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA”. preprint at aRxiv (2020). arXiv.

[67] 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). Virtual, July 5–10, 2020. pdf.

[66] 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).

[65] 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.

[64] A. Markham and M. Grosse-Wentrup. “Measurement Dependence Inducing Latent Causal Models”. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). Toronto, Canada, Aug. 3–6, 2020.

[63] 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). New York City, New York, USA, Feb. 7–12, 2020.

[62] C. Plant, S. Biedermann, and C. Böhm. “Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning”. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). San Diego, California, USA, Aug. 23–27, 2020.

[61] 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.

[60] M. Schneble and G. Kauermann. “Intensity Estimation on Geometric Networks with Penalized Splines”. preprint at arXiv (2020). arXiv.

[59] 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). Virtual Conference, Apr. 26–May 1, 2020. arXiv.

[58]        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). San Diego, California, USA, Aug. 23–27, 2020.


2019

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

[56] 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.

[55] 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.

[54] 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.

[53] 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.

[52] A. Beer, N. S. Schüler, and T. Seidl. “A Generator for Subspace Clusters”. In Proceedings of the Conference on Lernen. Wissen. Daten. Analysen (LWDA). Berlin, Germany,Sept. 30–Oct. 2, 2019. pdf.

[51] 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.

[50] 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.

[49] 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). Newark, New York, USA, Oct. 2–4, 2019.

[48] 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.

[47] 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). Vancouver, Canada, Dec. 8–14, 2019. arXiv.

[46] M. Binder, S. Dandl, and J. Moosbauer. “mosmafs: Multi-Objective Simultaneous Model and Feature Selection”. R package (2019).

[45] 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.

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

[43] A. Bojchevski and S. Günnemann. “Certifiable Robustness to Graph Perturbations”. In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS). Vancouver, Canada, Dec. 8–14, 2019. arXiv.

[42] 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). Thessaloniki, Greece, Oct. 14–17, 2019.

[41] 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). Newark, New York, USA, Oct. 2–4, 2019.

[40] 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.

[39] 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.

[38] 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.

[37] 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.

[36] 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.

[35] 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.

[34] 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.

[33] 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.

[32] J. Held, A. Beer, and T. Seidl. “Chain-detection Between Clusters”. Datenbank-Spektrum 19 (2019).

[31] 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). Rostock, Germany, Mar. 4–8, 2019.

[30] 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). Berlin, Germany, Sept. 30–Oct. 2, 2019. pdf.

[29] 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.

[28] 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.

[27] 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). Newark, New York, USA, Oct. 2–4, 2019.

[26] 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.

[25] 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.

[24] 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.

[23] 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.

[22] 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.

[21] 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). Berlin, Germany, Sept. 30–Oct. 2, 2019.

[20] J. Klicpera, S. Weißenberger, and S. Günnemann. “Diffusion Improves Graph Learning”. IIn Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS). Vancouver, Canada, Dec. 8–14, 2019. arxiv.

[19] G. König and M. Grosse-Wentrup. “A Causal Perspective on Challenges for AI in Precision Medicine”. In Proceedings of the 2nd International Congress on Precision Medicine (PMBC). Munich, Germany, Oct. 14–15, 2019.

[18] M. Lang, M. Binder, J. Richter, P. Schratz, F. Pfisterer, S. Coors, Q. A., G. Casalicchio, L. Kotthoff, and B. Bischl. “mlr3: A modern object-oriented machine learning framework in R”. Journal of Open Source Software 4.44 (2019).DOI

[17] M. Lotfollahi, F. A. Wolf, and F. J. Theis. “scGen predicts single-cell perturbation responses”. Nature Methods 16 (2019)

[16] F. Lüer, D. Mautz, and C. Böhm. “Anomaly Detection in Time Series using Generative Adversarial Networks”. In Proceedings of the IEEE International Conference on Data Mining Workshops (ICDM). Beijing, China, Nov. 8–11, 2019.

[15] D. Kazempour, M. Hunemörder, A. Beer, and T. Seidl. “CODEC - Detecting Linear Correlations in Dense Clusters with Comedian-based PCA”. In Proceedings of the Conference on Lernen. Wissen. Daten. Analysen (LWDA). Berlin, Germany, Sept. 30–Oct. 2, 2019. [pdf] (https://pdfs.semanticscholar.org/a840/06ae7f671e176177d1a10cc544ae5aea96fb.pdf)

[14] A. Markham and M. Grosse-Wentrup. “A Causal Semantics for the Edge Clique Cover Problem”. In Proceedings of the Workshop on Graphical Models: Conditional Independence and Algebraic Structures. TUM, Munich, Germany, Oct. 2019. eprint.

[13] A. Markham and M. Grosse-Wentrup. “Measurement Dependence Inducing Latent Causal Models”. In Proceedings of the Workshops of the 33rd Conference on Neural Information Processing Systems (NeurIPS). Vancouver, Canada, Dec. 8–14, 2019. arxiv.

[12] D. Mautz, C. Plant, and C. Böhm. “Deep Embedded Cluster Tree”. In Proceedings of the IEEE International Conference on Data Mining (ICDM). Beijing, China, Nov. 8–11, 2019.

[11] C. Molnar, G. Casalicchio, and B. Bischl. “Quantifying interpretability of arbitrary machine learning models through functional decomposition”. preprint at aRxiv (2019). arxiv.

[10] 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.

[9] F. Pfisterer, L. Beggel, X. Sun, F. Scheipl, and B. Bischl. “Benchmarking time series classification – Functional data vs machine learning approaches”. Technical report under revision, 2019. arxiv.

[8] F. Pfisterer, S. Coors, J. Thomas, and B. Bischl. “Multi-Objective Automatic Machine Learning with AutoxgboostMC”. In Proceedings of the Workshop On Automating Data Science of the European Conference on Machine Learning, Principles, and Practice of Knowledge Discovery in Databases (ECMLPKDD). Wuerzburg, Germany, Sept. 16–20, 2019. arxiv.

[7] 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.

[6] S. Schmoll, S. Friedl, and M. Schubert. “Scaling the Dynamic Resource Routing Problem”. In Proceedings of the 16th International Symposium on Spatial and Temporal Databases (SSTD). Vienna, Austria, Aug. 19–21, 2019. DOI.

[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]        F. A. Wolf, F. K. Hamey, M. Plass, J. Solana, J. S. Dahlin, B. Göttgens, N. Rajewsky, L. Simon, and F. J. Theis. “PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells”. Genome Biology 20.59 (2019). DOI.


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.