Publications


2021

[254] M. Ali, M. Berrendorf, C. T. Hoyt, L. Vermue, S. Sharifzadeh, V. Tresp and J. Lehmann. “PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings". Journal of Machine Learning Research 22.82 (2021). PDF.
[253] Q. Au, J. Herbinger, C. Stachl, B. Bischl and G. Casalicchio. “Grouped Feature Importance and Combined Features Effect Plot". Preprint at arXiv (2021). arXiv.
[252] M. Aygun, A. Osep, M. Weber, M. Maximov, C. Stachniss, J. Behley and L. Leal-Taixe. “4D panoptic segmentation". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[251] V. Bauer, D. Harhoff and G. Kauermann. “A smooth dynamic network model for patent collaboration data". Advances in Statistical Analysis (2021), to appear. arXiv.
[250] M. Becker, S. Gruber, J. Richter, J. Moosbauer and B. Bischl. “mlr3hyperband: Hyperband for 'mlr3'". 2021. MLR. GitHub.
[249] M. Becker, M. Lang, J. Richter, B. Bischl and D. Schalk. “mlr3tuning: Tuning for 'mlr3'". 2021. MLR. GitHub.
[248] M. Becker, J. Richter, M. Lang, B. Bischl and M. Binder. “bbotk: Black-Box Optimization Toolkit". 2021. MLR. GitHub.
[247] 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 on Extending Database Technology (EDBT 2021). Nicosia, Cyprus, 2021.
[246] M. Berrendorf, E. Faerman and V. Tresp. “Active Learning for Entity Alignment". In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021. arXiv.
[245] M. Berrendorf, L. Wacker and E. Faerman. “A Critical Assessment of State-of-the-Art in Entity Alignment". In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021.
[244] M. Binder. “mlrintermbo: Model-Based Optimization for 'mlr3' through 'mlrMBO'". 2021. R-package. GitHub.
[243] M. Binder, F. Pfisterer, M. Lang, L. Schneider, L. Kotthoff and B. Bischl. “mlr3pipelines - Flexible Machine Learning Pipelines in R". Journal of Machine Learning Research 22.184 (2021).
[242] B. Bischl, M. Binder, M. Lang, T. Pielok, J. Richter, S. Coors, J. Thomas, t. Ullmann, M. Becker, A.-L. Boulesteix, D. Deng and M. Lindauer. “Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges". Preprint at arXiv (2021). arXiv.
[241] L. Bothmann, S. Strickroth, G. Casalicchio, D. Rügamer, M. Lindauer, F. Scheipl and B. Bischl. “Developing Open Source Educational Resources for Machine Learning and Data Science". Preprint at arXiv (2021). arXiv.
[240] A. Božič, P. Palafox, M. Zollhöfer, J. Thies, A. Dai and M. Nießner. “Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[239] D. Z. Chen, A. Gholami, M. Nießner and A. X. Chang. “Scan2Cap: Context-aware Dense Captioning in RGB-D Scans". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[238] A. Dai, Y. Siddiqui, J. Thies, J. Valentin and M. Nießner. “SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[237] M. Eisenberger, D. Novotny, G. Kerchenbaum, P. Labatut, N. Neverova, D. Cremers and A. Vedaldi. “NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[236] 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.
[235] T. Frerix, D. Kochkov, J. Smith, D. Cremers, M. Brenner and S. Hoyer. “Variational Data Assimilation with a Learned Inverse Observation Operator". In Proceedings of the 38th International Conference on Machine Learning (ICML 2021). Virtual, 2021. arXiv.
[234] 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.
[233] G. Gafni, J. Thies, M. Zollhöfer and M. Nießner. “Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[232] M. Gao, Z. Lähner, J. Thunberg, D. Cremers and F. Bernard. “Isometric Multi-Shape Matching". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[231] I. Gerostathopoulos, F. Plášil, C. Prehofer, J. Thomas and B. Bischl. “Automated Online Experiment-Driven Adaptation--Mechanics and Cost Aspects". IEEE Access 9 (2021).
[230] P. Gijsbers, F. Pfisterer, J. van Rijn, B. Bischl and J. Vanschoren. “Meta-Learning for Symbolic Hyperparameter Defaults". In Proceedings of the 2021 Genetic and Evolutionary Computation Conference (GECCO 2021). Lile, France, 2021.
[229] J. Goschenhofer, R. Hvingelby, D. Rügamer, J. Thomas, M. Wagner and B. Bischl. “Deep Semi-Supervised Learning for Time Series Classification". Preprint at arXiv (2021). arXiv.
[228] J. Hou, B. Graham, M. Nießner and S. Xie. “Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[227] 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, 2021. arXiv.
[226] P. Kopper, S. Pölsterl, C. Wachinger, B. Bischl, A. Bender and D. Rügamer. “Semi-Structured Deep Piecewise Exponential Models". In Proceedings of the AAAI Spring Symposium Series on Survival Prediction: Algorithms, Challenges and Applications (AAAI-SPACA 2021). Palo Alto, California, USA, 2021. arXiv.
[225] M. Lang. “mlr3measures: Performance Measures for 'mlr3'". 2021. R-package.
[224] M. Lang, B. Bischl, J. Richter, X. Sun and M. Binder. “paradox: Define and Work with Parameter Spaces for Complex Algorithms". 2021. MLR. GitHub.
[223] M. Lebacher, P. W. Thurner and G. Kauermann. “Censored regression for modelling small arms trade volumes and its ‘Forensic’ use for exploring unreported trades". Journal of the Royal Statistical Society. Series C (Applied Statistics) (OnlineFirst) (2021). DOI.
[222] M. Lotfollahi, A. K. Susmelj, C. De Donno, Y. Ji, I. L. Ibarra, F. A. Wolf, N. Yakubova, F. J. Theis and D. Lopez-Paz. “Compositional perturbation autoencoder for single-cell response modeling". preprint at bioRxiv (2021). DOI.
[221] Y. Ma and V. Tresp. “Causal Inference under Networked Interference and Intervention Policy Enhancement". In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021). Virtual, 2021.
[220] N. Müller, Y.-S. Wong, N. J. Mitra, A. Dai and M. Nießner. “Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[219] P. Müller, V. Golkov, V. Tomassini and D. Cremers. “Rotation-Equivariant Deep Learning for Diffusion MRI (short version)". In Proceedings of the International Society for Magnetic Resonance in Medicine Annual Meeting (ISMRM 2021). Virtual, 2021, long version in arXiv. arXiv.
[218] G. De Nicola, B. Sischka and G. Kauermann. “Mixture Models and Networks -- Overview of Stochastic Blockmodelling". Statistical Modelling (2021), to appear.
[217] Y. Nie, J. Hou, X. Han and M. Nießner. “RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.
[216] 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, 2021.
[215] F. Pargent, F. Pfisterer, J. Thomas and B. Bischl. “Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features". Preprint at arXiv (2021). arXiv.
[214] F. Pfisterer, J. van Rijn, P. Probst, A. Müller and B. Bischl. “Learning Multiple Defaults for Machine Learning Algorithms". In Proceedings of the 2021 Genetic and Evolutionary Computation Conference (GECCO 2021). Lile, France, 2021. arXiv.
[213] P. Wenzel Q. Khan and D. Cremers. “Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry". In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021). Virtual, 2021.
[212] D. Rügamer, F. Pfisterer and P. Baumann. “deepregression: Fitting Semi-Structured Deep Distributional Regression in R". 2021. GitHub.
[211] D. Rügamer, R. Shen, C. Bukas, L. B. de Andrade e Sousa, D. Thalmeier, N. Klein, C. Kolb, F. Pfisterer, P. Kopper, B. Bischl and C. L. Müller. “deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression". Preprint at arXiv (2021). arXiv.
[210] P. Schratz and M. Becker. “mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'". 2021. R-package.
[209] J. Schuchardt, A. Bojchevski, J. Klicpera and S. Günnemann. “Collective Robustness Certificates - Exploiting Interdependence in Graph Neural Networks". In Proceedings of the 9th International Conference on Learning Representations (ICLR 2021). Virtual, 2021.
[208] H. Seibold, A. Charlton, A.-L. Boulesteix and S. Hoffmann. “Statisticians roll up your sleeves! There’s a crisis to be solved". Significance (2021), in press. DOI.
[207] S. Sharifzadeh, S. M. Baharlou and V.r Tresp. “Classification by Attention: Scene Graph Classification with Prior Knowledge". In Proceedings of the 35th Conference on Artificial Intelligence (AAAI 2021). Virtual, 2021.
[206] R. Sonabend, F. J. Király, A. Bender, B. Bischl and M. Lang. “mlr3proba: An R Package for Machine Learning in Survival Analysis". Bioinformatics btab039 (2021). DOI.
[205] C. Tomani, D. Cremers and F. Buettner. “Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration". Preprint at arXiv (2021). arXiv.
[204] C. Tomani, S. Gruber, M. E. Erdem, D. Cremers and F. Buettner. “Post-hoc Uncertainty Calibration for Domain Drift Scenarios". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021. arXiv.
[203] T. Ullmann, C. Hennig and A.-L. Boulesteix. “Validation of cluster analysis results on validation data: A systematic framework". Preprint at arXiv (2021). arXiv.
[202] A. Volkmann, A. Stöcker, F. Scheipl and S. Greven. “Multivariate Functional Additive Mixed Models". preprint at arXiv (under revision) (2021). arXiv.
[201] 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 travel distances". Tourism Economics (2021). DOI.
[200] J. Wrobel and A. Bauer. “registr 2.0: Incomplete Curve Registration for Exponential Family Functional Data". The Journal of Open Source Software 6.61 (2021). DOI.



2020

[199] M. Berrendorf and E. Faerman. “mberr/ea-active-learning: Zenodo". 2020. DOI.
[198] M. Berrendorf, L. Wacker and E. Faerman. “mberr/ea-sota-comparison: Zenodo". 2020. DOI.
[197] M. Ali, C. T. Hoyt, L. Vermue, M. Galkin and M. Berrendorf. “pykeen/benchmarking". 2020. DOI.
[196] 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.
[195] M. Ali, M. Berrendorf, C. T. Hoyt, L. Vermue, M. Galkin, S. Sharifzadeh, A. Fischer, V. Tresp and J. Lehmann. “Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework". CoRR (2020). arXiv.
[194] M. Ali, M. Berrendorf, C. T. Hoyt, L. Vermue, S. Sharifzadeh, V. Tresp and J. Lehmann. “PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings". CoRR (2020). arXiv.
[193] 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, 2020. DOI.
[192] A. Athar, S. Mahadevan, A. Osep, L. Leal-Taixe and B. Leibe. “STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos". In Proceedings of the 16th European Conference on Computer Vision (ECCV 2020). Virtual, 2020.
[191] M. Aygün, Z. Lähner and D. Cremers. “Unsupervised Dense Shape Correspondence using Heat Kernels". In Proceedings of the 8th International Conference on 3D Vision (3DV 2020). Virtual, 2020.
[190] P. F. M. Baumann, T. Hothorn and D. Rügamer. “Deep Conditional Transformation Models". Preprint at arXiv (2020). arXiv.
[189] M. Becker, P. Schratz, M. Lang and B. Bischl. “mlr3fselect: Feature Selection for 'mlr3'". 2020. R-package.
[188] 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, 2020.
[187] 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, 2020. PDF.
[186] 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, 2020.
[185] 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 and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020). Virtual, 2020. arXiv.
[184] P. Bergmann, T. Meinhardt and L. Leal-Taixe. “Tracking without bells and whistles". In Proceedings of the 14th International Conference on Computer Vision (ICCV 2020). Venice, Italy, 2020.
[183] 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, 2020. arXiv. GitHub.
[182] M. Berrendorf, E. Faerman and V. Tresp. “Active Learning for Entity Alignment". In Proceedings of the 5th International Workshop on Deep Learning for Graphs (DL4G@WWW2020). Taipeh, Taiwan, 2020.
[181] M. Berrendorf, E. Faerman, L. Vermue and V. Tresp. “Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank". In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020). Virtual, 2020.
[180] M. Berrendorf, E. Faerman, L. Vermue and V. Tresp. “Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank (Extended Abstract)". In Proceedings of the 5th International Workshop on Deep Learning for Graphs (DL4G@WWW2020). Taipeh, Taiwan, 2020.
[179] M. Berrendorf, E. Faerman, L. Vermue and V. Tresp. “On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods". The Computing Research Repository (CoRR) (2020). arXiv.
[178] M. Berrendorf, L. Wacker and E. Faerman. “A Critical Assessment of State-of-the-Art in Entity Alignment". CoRR (2020). arXiv.
[177] 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, 2020.
[176] 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.
[175] 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, 2020. DOI.
[174] M. Binder, F. Pfisterer and B. Bischl. “Collecting empirical data about hyperparameters for data driven AutoML". In Proceedings of the 7th ICML Workshop on Automated Machine Learning (AutoML 2020). Vienna, Austria, 2020. arXiv.
[173] M. Binder, F. Pfisterer, L. Schneider, B. Bischl, M. Lang and S. Dandl. “mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'". 2020. MLR. GitHub.
[172] 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, 2020.
[171] C. Böhm and C. Plant. “Massively Parallel Random Number Generation". In Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2020). Virtual, 2020.
[170] A. Bommert, X. Sun, B. Bischl, J. Rahnenführer and M. Lang. “Benchmark for filter methods for feature selection in high-dimensional classification data". Computational Statistics and Data Analysis 143 (2020).
[169] 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, 2020.
[168] A.-L. Boulesteix, A. Charlton, S. Hoffmann and H. Seibold. “A replication crisis in methodological research?". Significance 17.5 (2020). DOI.
[167] G. Brasó and L. Leal-Taixé. “Learning a Neural Solver for Multiple Object Tracking". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020. arXiv.
[166] J. Busch, E. Faerman, M. Schubert and T. Seidl. “Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering". In Proceedings of the Workshop on Self-Supervised Learning - Theory and Practice (SSL 2020) at the 34rd Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020. arXiv.
[165] T. Czempiel, M. Paschali, M. Keicher, W. Simson, H. Feussner, S. T. Kim and N. Navab. “TeCNO: Surgical Phase Recognition with Multi-stage Temporal Convolutional Network". In Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020). Virtual, 2020.
[164] 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, 2020. DOI.
[163] 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.
[162] N. Demmel, M. Gao, E. Laude, T. Wu and D. Cremers. “Distributed Photometric Bundle Adjustment". In Proceedings of the 8th International Conference on 3D Vision (3DV 2020). Virtual, 2020.
[161] S. Denner, A. Khakzar, M. Sajid, M. Saleh, Z. Spiclin, S. T. Kim and N. Navab. “Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation". In Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020). Virtual, 2020.
[160] H. Dhamo, A. Farshad, I. Laina, N. Navab, G. D. Hager, F. Tombari and C. Rupprecht. “MultiSemantic Image Manipulation Using Scene Graphs". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020.
[159] L. Dony, M. König, D. Fischer and F. J. Theis. “Variational autoencoders with flexible priors enable robust distribution learning on single-cell RNA sequencing data". In Proceedings of the ICML Workshop on Computational Biology (WCB 2020). Virtual, 2020.
[158] J. Du, R. Wang and D. Cremers. “DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization". In Proceedings of the 16th European Conference on Computer Vision (ECCV 2020). Virtual, 2020.
[157] M. Eisenberger, Z. Lähner and D. Cremers. “Smooth Shells: Multi-Scale Shape Registration with Functional Maps". In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020.
[156] M. Eisenberger, A. Toker, L. Leal-Taixé and D. Cremers. “Deep Shells: Unsupervised Shape Correspondence with Optimal Transport". In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020.
[155] 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).
[154] G. Fabbro, V. Golkov, T. Kemp and D. Cremers. “Speech Synthesis and Control Using Differentiable DSP". Preprint at arXiv (2020). arXiv.
[153] 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, 2020.
[152] T. Frerix, M. Nießner and D. Cremers. “Homogeneous Linear Inequality Constraints for Neural Network Activations". In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2020). Virtual, 2020.
[151] S. Friedl, S. Schmoll, F. Borutta and M. Schubert. “SMART-Env". In Proceedings of the 321st IEEE International Conference on Mobile Data Management (MDM 2020). Versailles, France, 2020. DOI.
[150] C. Fritz and G. Kauermann. “On the Interplay of Regional Mobility, Social Connectedness, and the Spread of COVID-19 in Germany". Preprint at arXiv (2020). arXiv.
[149] C. Fritz, M. Lebacher and G. Kauermann. “Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time". Statistica Neerlandica 74.3 (2020). DOI.
[148] C. Fritz, P. W. Thurner and G. Kauermann. “Separable and Semiparametric Network-based Counting Processes applied to the International Combat Aircraft Trades". Preprint at arXiv (2020). arXiv.
[147] M. Fromm, E. Faerman, M. Berrendorf, S. Bhargava, R. Qi, Y. Zhang, L. Dennert, S. Selle, Y. Mao and T. Seidl. “Argument Mining Driven Analysis of Peer-Reviews". Preprint at arXiv (2020). arXiv.
[146] S. Geisler, D. Zügner and S. Günnemann. “Reliable Graph Neural Networks via Robust Aggregation". In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020.
[145] V. Golkov, A. Becker, D. T. Plop, D. Čuturilo, N. Davoudi, J. Mendenhall, R. Moretti, J. Meiler and D. Cremers. “Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions". Preprint at arXiv (2020). arXiv.
[144] M. Herrmann. “fda-ndr: Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. R package". 2020.
[143] M. Herrmann. “manifun: Collection of functions to work with embeddings and functional data. R package". 2020.
[142] 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.
[141] M. Herrmann and F. Scheipl. “Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction". Preprint at arXiv (2020). arXiv.
[140] M. Hildebrandt, J. A. Q. Serna, Y. Ma, M. Ringsquandl, M. Joblin and V. Tresp. “Simulated Annealing for 3D Shape Correspondence". In Proceedings of the 34th Conference on Artificial Intelligence (AAAI 2020). New York City, New York, USA, 2020.
[139] O. G. Holmberg, N. D. Köhler, T. Martins, J. Siedlecki, T. Herold, L. Keidel, B. Asani, J. Schiefelbein, S. Priglinger, K. U. Kortuem and F. J. Theis. “Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy". Nature Machine Intelligence 2.11 (2020).
[138] B. Holzschuh, Z. Lähner and D. Cremers. “Simulated Annealing for 3D Shape Correspondence". In Proceedings of the 8th International Conference on 3D Vision (3DV 2020). Virtual, 2020.
[137] 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, 2020. PDF.
[136] 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, 2020. DOI.
[135] 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, 2020. DOI.
[134] 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, 2020. DOI.
[133] 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 ICDM Workshop on High Dimensional Data Mining (HDM 2020) at the 20th IEEE International Conference on Data Mining (ICDM 2020). Virtual, 2020. DOI.
[132] D. Kazempour, P. Kröger and T. Seidl. “Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering". In Proceedings of the 8th ICDM Workshop on High Dimensional Data Mining (HDM 2020) at the 20th IEEE International Conference on Data Mining (ICDM 2020). Virtual, 2020.
[131] 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 ICDM Workshop on High Dimensional Data Mining (HDM 2020) at the 20th IEEE International Conference on Data Mining (ICDM 2020). Virtual, 2020.
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2019

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2018

[10] G. Casalicchio, C. Molnar and B. Bischl. “Visualizing the feature importance for black box models". In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018). Dublin, Ireland, 2018.
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[3] D. Rügamer, S. Brockhaus, K. Gentsch, K. Scherer and S. Greven. “Boosting factor-specific functional historical models for the detection of synchronization in bioelectrical signals". Journal of the Royal Statistical Society. Series C (Applied Statistics) 67.3 (2018). DOI.
[2] D. Schalk, J. Thomas and B. Bischl. “compboost: Modular Framework for Component-wise Boosting". The Journal of Open Source Software 3.30 (2018).
[1] J. Thomas, S. Coors and B. Bischl. “Automatic gradient boosting". Preprint at arXiv (2018). arXiv.