Publications


2022

[324] M. Ali, M. Berrendorf, M. Galkin, V. Thost, T. Ma, V. Tresp and J. Lehmann. “Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)”. Best paper track at the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022). Vienna, Austria, 2022.
[323] D. Alivanistos, M. Berrendorf, M. Cochez and M. Galkin. “Query Embedding on Hyper-Relational Knowledge Graphs”. In Proceedings of the 10th International Conference on Learning Representations (ICLR 2022). Virtual, 2022. PDF. GitHub.
[322] S. Dandl, F. Pfisterer and B. Bischl. “Multi-Objective Counterfactual Fairness”. In Proceedings of the 2022 Genetic and Evolutionary Computation Conference (GECCO 2022). Boston, Massachusetts, USA, 2022. DOI.
[321] G. De Nicola, B. Sischka and G. Kauermann. “Mixture Models and Networks: The Stochastic Block Model”. Statistical Modelling 22.1-2 (2022). DOI.
[320] Z. A. Farsani and V. J. Schmid. “Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis”. Entropy 24.2 (2022). DOI.
[319] C. Frey, Y. Ma and M. Schubert. “SEA: Graph Shell Attention in Graph Neural Networks”. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022.
[318] C. Fritz, E. Dorigatti and D. Rügamer. “Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany”. Scientific Reports 12.3930 (2022). arXiv.
[317] C. Fritz and G. Kauermann. “On the Interplay of Regional Mobility, Social Connectedness, and the Spread of COVID-19 in Germany”. Journal of the Royal Statistical Society. Series A (Statistics in Society) 185.1 (2022). arXiv.
[316] G. Fu, Z. Meng, Y. Ma, Z. Han, Z. Ding, M. Schubert and R. Wattenhofer. “TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion”. In Proceedings of the 6th ACL Workshop on Structured Prediction for NLP (SPNLP 2022) at the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022). Dublin, Ireland, 2022.
[315] M. Galkin, M. Berrendorf and C. T. Hoyt. “An Open Challenge for Inductive Link Prediction on Knowledge Graphs”. In Proceedings of the 2022 Workshop on Graph Learning Benchmarks (GLB 2022) at the International World Wide Web Conference (WWW 2022). Virtual, 2022. arXiv. GitHub.
[314] S. Gilhuber, M. Berrendorf, Y. Ma and T. Seidl. “Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations”. In Proceedings of the 6th International Workshop on Interactive Adaptive Learning (IAL2022) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022). Grenoble, France, 2022. GitHub.
[313] E. Hohma, C. Frey, A. Beer and T. Seidl. “SCAR - Spectral Clustering Accelerated and Robustified”. In Proceedings of the 48th International Conference on Very Large Databases (VLDB 2022). Sydney, Australia (and hybrid), 2022.
[312] C. T. Hoyt, M. Berrendorf, M. Gaklin, V. Tresp and B. M. Gyori. “A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs”. In Proceedings of the 2022 Workshop on Graph Learning Benchmarks (GLB 2022) at the International World Wide Web Conference (WWW 2022). Virtual, 2022. arXiv.
[311] S. Kevork and G. Kauermann. “Bipartite Exponential Random Graph Models with Nodal Random Effects”. Social Networks 70 (2022). DOI.
[310] S. Kevork and G. Kauermann. “Iterative Estimation of Mixed Exponential Random Graph Models with Nodal Random Effects”. Network Science 9.4 (2022). DOI.
[309] P. Kopper, S. Wiegrebe, B. Bischl, A. Bender and D. Rügamer. “DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis”. In Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022). Chengdu, China, 2022. arXiv.
[308] 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-cell fate mapping”. Nature Methods 19.2 (2022). DOI.
[307] C. Leiber, D. Mautz, C. Plant and C. Böhm. “Automatic Parameter Selection for Non-Redundant Clustering”. In Proceedings of the SIAM International Conference on Data Mining (SDM 2022). Virtual, 2022.
[306] C. Nießl, M. Herrmann, C. Casalicchio and A.-L. Boulesteix. “Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12.2 (2022). DOI.
[305] F. Pfisterer, L. Schneider, Moosbauer, M. Binder and B. Bischl. “YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization”. In Proceedings of the 1st International Conference on Automated Machine Learning (AutoML-Conf 2022) co-located with the 39th International Conference on Machine Learning (ICML 2022). Baltimore, Maryland, USA, 2022. arXiv.
[304] L. Schneider, F. Pfisterer, P. Kent, J. Branke, B. Bischl and J. Thomas. “Tackling neural architecture search with quality diversity optimization”. In Proceedings of the 1st International Conference on Automated Machine Learning (AutoML-Conf 2022) co-located with the 39th International Conference on Machine Learning (ICML 2022). Baltimore, Maryland, USA, 2022. arXiv.
[303] L. Schneider, F. Pfisterer, J. Thomas and B. Bischl. “A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models”. In Proceedings of the 2022 Genetic and Evolutionary Computation Conference (GECCO 2022). Boston, Massachusetts, USA, 2022. DOI.
[302] C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl and C. Heumann. “Marginal Effects for Non-Linear Prediction Functions”. Preprint at arXiv (2022), Under review. arXiv.
[301] S. Sharifzadeh, S. M. Baharlou, M. Schmitt, H. Schütze and V. Tresp. “Improving Scene Graph Classification by Exploiting Knowledge from Texts”. In Proceedings of the 36th Conference on Artificial Intelligence (AAAI 2022). Virtual, 2022. arXiv.
[300] B. Sischka and G. Kauermann. “EM-Based Smooth Graphon Estimation Using MCMC and Spline-Based Approaches”. Social Networks 68 (2022). DOI.
[299] N. Strauß, D. Winkel, M. Berrendorf and M. Schubert. “Reinforcement Learning for Multi-Agent Stochastic Resource Collection”. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022.
[298] T. Ullmann, A. Beer, M. Hünemörder, T. Seidl and A.-L. Boulesteix. “Over-optimistic evaluation and reporting of novel cluster algorithms: An illustrative study”. Advances in Data Analysis and Classification (2022). DOI.
[297] T. Ullmann, C. Hennig and A.-L. Boulesteix. “Validation of cluster analysis results on validation data: A systematic framework”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12.3 (2022). arXiv.
[296] M. Windl, S. S. Feger, L. Zijlstra, A. Schmidt and P. W. Wozniak. “T‘It Is Not Always Discovery Time’: Four Pragmatic Approaches in Designing AI Systems”. In Proceedings of the Conference on Human Factors in Computing Systems (CHI 2022). New Orleans, LA, USA, 2022. DOI.
[295] M. Windl and S. Mayer. “The Skewed Privacy Concerns of Bystanders in Smart Environments”. In Proceedings of the ACM International Conference on Mobile Human-Computer Interaction (MobileHCI 2022). Vancouver, Canada, 2022.
[294] D. Winkel, N. Strauß, M. Schubert and T. Seidl. “Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection”. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022.



2021

[293] M. Ali, M. Berrendorf, M. Galkin, V. Thost, T. Ma, V. Tresp and J. Lehmann. “Improving Inductive Link Prediction Using Hyper-Relational Facts”. In Proceedings of the 20th International Semantic Web Conference (ISWC 2021). Virtual, 2021. arXiv. GitHub.
[292] 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”. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021). DOI. GitHub.
[291] 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.
[290] Q. Au, J. Herbinger, C. Stachl, B. Bischl and G. Casalicchio. “Grouped Feature Importance and Combined Features Effect Plot”. Preprint at arXiv (2021). arXiv.
[289] 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.
[288] V. Bauer, D. Harhoff and G. Kauermann. “A smooth dynamic network model for patent collaboration data”. Advances in Statistical Analysis (2021), to appear. arXiv.
[287] M. Becker, S. Gruber, J. Richter, J. Moosbauer and B. Bischl. “mlr3hyperband: Hyperband for 'mlr3'”. 2021. MLR. GitHub.
[286] M. Becker, M. Lang, J. Richter, B. Bischl and D. Schalk. “mlr3tuning: Tuning for 'mlr3'”. 2021. MLR. GitHub.
[285] M. Becker, J. Richter, M. Lang, B. Bischl and M. Binder. “bbotk: Black-Box Optimization Toolkit”. 2021. MLR. GitHub.
[284] 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.
[283] A. Beer, L. Stephan and T. Seidl. “LUCKe- Connecting Clustering and Correlation Clustering”. In Proceedings of the 9th ICDM Workshop on High Dimensional Data Mining (HDM 2021) in conjunction with the 21st IEEE International Conference on Data Mining (ICDM 2021). Auckland, New Zealand, 2021. DOI.
[282] M. Bernhard and M. Schubert. “Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications”. Remote Sensing (2021). URL.
[281] 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. GitHub.
[280] 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. arXiv. GitHub.
[279] M. Binder. “mlrintermbo: Model-Based Optimization for 'mlr3' through 'mlrMBO'”. 2021. R-package. GitHub.
[278] 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).
[277] 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.
[276] C. Böhm, M. Perdacher and C. Plant. “A Novel Hilbert Curve for Cache-Locality Preserving Loops”. IEEE Transactions on Big Data 7.2 (2021).
[275] 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.
[274] 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.
[273] 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.
[272] Z. Chongyu. “GPU-based Data Mining on Android Devices”. .
[271] S. Coors, D. Schalk, B. Bischl and D. Rügamer. “Automatic Componentwise Boosting: An Interpretable AutoML System”. In Proceedings of the Automating Data Science Workshop (ADS 2021) at the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD 2021). Virtual, 2021. arXiv.
[270] 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.
[269] P. Dufter, N. Kassner and H. Schütze. “Static Embeddings as Efficient Knowledge Bases?”. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2021). Virtual, 2021. PDF.
[268] 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.
[267] Y. Elazar, N. Kassner, S. Ravfogel, A. Ravichander, E. Hovy, H. Schütze and Y. Goldberg. “Measuring and Improving Consistency in Pretrained Language Models”. Transactions of the Association for Computational Linguistics 9 (2021). arXiv.
[266] M. P. Fabritius, M. Seidensticker, J. Rueckel, C. Heinze, M. Pech, K. J. Paprottka, P. M. Paprottka, J. Topalis, A. Bender, J. Ricke, A. Mittermeier and M. Ingrisch. “Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer”. Journal of Clinical Medicine 10.16 (2021). DOI.
[265] 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.
[264] C. Fritz, M. Mehrl, P. W. Thurner and G. Kauermann. “The Role of Governmental Weapons Procurements in Forecasting Monthly Fatalities in Intrastate Conflicts: A Semiparametric Hierarchical Hurdle Model”. International Interactions (2021). DOI.
[263] C. Fritz, P. W. Thurner and G. Kauermann. “Separable and Semiparametric Network-based Counting Processes applied to the International Combat Aircraft Trades”. Network Science 9.3 (2021). arXiv.
[262] M. Fromm, M. Berrendorf, S. Obermeier, T. Seidl and E. Faerman. “Diversity Aware Relevance Learning for Argument Search”. In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021. arXiv. GitHub.
[261] 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”. In Proceedings of the 35th Conference on Artificial Intelligence (AAAI 2021). Virtual, 2021. arXiv. GitHub.
[260] 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.
[259] 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.
[258] S. Garg, H. Dhamo, A. Farshad, S. Musatian, N. Navab and F. Tombari. “Unconditional Scene Graph Generation”. In Proceedings of the 15th International Conference on Computer Vision (ICCV 2021). Virtual, 2021.
[257] I. Gerostathopoulos, F. Plášil, C. Prehofer, J. Thomas and B. Bischl. “Automated Online Experiment-Driven Adaptation--Mechanics and Cost Aspects”. IEEE Access 9 (2021).
[256] 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.
[255] 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.
[254] M. Herrmann and F. Scheipl. “A Geometric Perspective on Functional Outlier Detection”. Stats 4.4 (2021). DOI.
[253] M. Herrmann and F. Scheipl. “Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction”. Preprint at arXiv (2021). arXiv.
[252] 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.
[251] 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.
[250] N. Kassner, O. Tafjord, H. Schütze and P. Clark. “BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief”. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). Punta Cana, Dominican Republic, 2021. DOI.
[249] D. Kazempour, A. Beer, M. Oelker, P. Kröger and T. Seidl. “Compound Segmentation via Clustering on Mol2Vec-based Embeddings”. In Proceedings of the 17th IEEE eScience Conference (eScience2021). Virtual, 2021. DOI.
[248] N. Kees, M. Fromm, E. Faerman and T. Seidl. “Active Learning for Argument Strength Estimation”. In Proceedings of the 2nd Workshop on Insights from Negative Results colocated with the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). Punta Cana, Dominican Republic, 2021. arXiv.
[247] A. Khakzar, S. Baselizadeh, S. Khanduja, C. Rupprecht, S. T. Kim and N. Navab. “Neural Response Interpretation through the Lens of Critical Pathways”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021. PDF.
[246] A. Khakzar, S. Musatian, J. Buchberger, I. V. Quiroz, N. Pinger, S. Baselizadeh, S. T. Kim and N. Navab. “Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models”. In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). Strasbourg, France, 2021. DOI.
[245] A. Khakzar, Y. Zhang, W. Mansour, Y. Cai, Y. Li, Y. Zhang, S. T. Kim and N. Navab. “Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features”. In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). Strasbourg, France, 2021. DOI.
[244] Q. Khan, P. Wenzel 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.
[243] S. T. Kim, L. Goli, M. Paschali, A. Khakzar, M. Keicher, T. Czempiel, E. Burian, R. Braren, N. Navab and T. Wendler. “Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs”. In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). Strasbourg, France, 2021. DOI.
[242] G. König, T. Freiesleben and M. Grosse-Wentrup. “A causal perspective on meaningful and robust algorithmic recourse”. Preprint at arXiv (2021). arXiv.
[241] 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.
[240] M. Lang. “mlr3measures: Performance Measures for 'mlr3'”. 2021. R-package.
[239] M. Lang, B. Bischl, J. Richter, X. Sun and M. Binder. “paradox: Define and Work with Parameter Spaces for Complex Algorithms”. 2021. MLR. GitHub.
[238] 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.
[237] C. Leiber, L. G. M. Bauer, B. Schelling, C. Böhm and C. Plant. “Dip-based Deep Embedded Clustering with k-Estimation”. In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). Singapore, 2021.
[236] A. Lohrer, A. Beer, M. Hünemörder, J. Lauterbach, T. Seidl and P. Kröger. “AnyCORE - An Anytime Algorithm for Cluster Outlier REmoval”. In Proceedings of the Conference on Lernen. Wissen. Daten. Analysen (LWDA 2021). München, Germany, 2021.
[235] A. Lohrer, J. Deller, M. Hünemörder and P. Kröger. “OAB - An Open Anomaly Benchmark Framework for Unsupervised and Semisupervised Anomaly Detection on Image and Tabular Data Sets”. In Proceedings of the 21st IEEE International Conference on Data Mining Workshops (ICDMW 2021). Auckland, New Zealand, 2021. DOI.
[234] 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 bioarXiv (2021). DOI.
[233] 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.
[232] A. Markham, R. Das and M. Grosse-Wentrup. “A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations”. In Proceedings of the 1st Conference on Causal Learning and Reasoning (CLeaR 2022). Eureka, CA, USA, 2021. arXiv.
[231] L. Miklautz, L. G. M. Bauer, D. Mautz, S. Tschiatschek, C. Böhm and C. Plant. “Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces”. In Proceedings of the 30th International Joint Conference on Artificial Intelligence ((IJCAI 2021)). Montreal, Canada, 2021.
[230] C. Molnar, T. Freiesleben, G. König, G. Casalicchio, M. N. Wright and B. Bischl. “Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process”. Preprint at arXiv (2021), Under review. arXiv.
[229] J. Moosbauer, M. Binder, L. Schneider, F. Pfisterer, M. Becker, M. Lang, L. Kotthoff and B. Bischl. “Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers”. Preprint at arXiv (2021). arXiv.
[228] J. Moosbauer, J. Herbinger, G. Casalicchio, M. Lindauer and B. Bischl. “Explaining Hyperparameter Optimization via Partial Dependence Plots (Poster)”. In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021. PDF. GitHub.
[227] 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.
[226] 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.
[225] 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.
[224] 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. DOI.
[223] 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.
[222] F. Pfisterer, C. Kern, S. Dandl, M. Sun, M. P. Kim and B. Bischl. “mcboost: Multi-Calibration Boosting for R”. The Journal of Open Source Software 6.64 (2021). DOI.
[221] 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.
[220] F. Pfisterer, L. Schneider, J. Moosbauer, M. Binder and B. Bischl. “YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization”. Preprint at arXiv (2021), Under review. arXiv.
[219] A. Python, A. Bender, M. Blangiardo, J. B. Illian, Y. Lin, B. Liu, T. C.D. Lucas, S. Tan, Y. Wen, D. Svanidze and J. Yin. “A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales”. Journal of the Royal Statistical Society. Series A (Statistics in Society) (OnlineFirst) (2021). DOI.
[218] A. Python, A. Bender, A. K. Nandi, P. A. Hancock, R. Arambepola, J. Brandsch and T. C. D. Lucas. “Predicting non-state terrorism worldwide”. Science Advances 7.31 (2021). DOI.
[217] L. Qian, C. Plant and C. Böhm. “Density-based Clustering for Adaptive Density Variation”. In Proceedings of the 21st IEEE International Conference on Data Mining (ICDM 2021). Auckland, New Zealand, 2021.
[216] M. Rezaei, E. Dorigatti, D. Rügamer and B. Bischl. “Learning Statistical Representation with Joint Deep Embedded Clustering”. Preprint at arXiv (2021). arXiv.
[215] D. Rügamer, F. Pfisterer and P. Baumann. “deepregression: Fitting Semi-Structured Deep Distributional Regression in R”. 2021. GitHub.
[214] 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.
[213] D. Schalk, B. Bischl and D. Rügamer. “Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization”. Preprint at arXiv (2021). arXiv.
[212] S. Schmoll. “Navigation with uncertain spatio-temporal resources”. . DOI.
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[85] R. Sonabend, F. J. Király, A. Bender, B. Bischl and M. Lang. “mlr3proba: Machine Learning Survival Analysis in R”. Preprint at arXiv (2020). arXiv.
[84] 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.
[83] L. von Stumberg, P. Wenzel, Q. Khan and D. Cremers. “GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization”. IEEE Robotics and Automation Letters 5.2 (2020).
[82] L. von Stumberg, P. Wenzel, N. Yang and D. Cremers. “LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization”. In Proceedings of the 8th International Conference on 3D Vision (3DV 2020). Virtual, 2020.
[81] P. Wenzel, R. Wang, N. Yang, Q. Cheng, Q. Khan, L. Stumberg, N. Zeller and D. Cremers. “4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving”. In Proceedings of the 42nd German Conference on Pattern Recognition (DAGM-GCPR 2020). Tübingen, Germany, 2020.
[80] F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller and D. Cremers. “MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020.
[79] J. Wrobel, A. Bauer, J. Goldsmith, E. McDonnel and F. Scheipl. “registr: Curve Registration for Exponential Family Functional Data. R package”. 2020.
[78] Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe and X. Alameda-Pineda. “How To Train Your Deep Multi-Object Tracker”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020.
[77] N. Yang, L. von Stumberg, R. Wang and D. Cremers. “D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020.
[76] Z. Ye, T. Möllenhoff, T. Wu and D. Cremers. “Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning”. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020). Virtual, 2020.
[75] Y. Yeganeh, A. Farshad, N. Navab and S. Albarqouni. “Inverse Distance Aggregation for Federated Learning with Non-IID Data”. In Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020). Virtual, 2020.
[74] 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, 2020.



2019

[73] 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.
[72] 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). Orlando, Florida, USA, 2019.
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[70] 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, 2019. DOI.
[69] 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, 2019.
[68] 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, 2019. PDF.
[67] 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, 2019. DOI.
[66] L. Beggel, M. Pfeiffer and B. Bischl. “Robust Anomaly Detection in Images Using Adversarial Autoencoders”. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019. PDF.
[65] 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 bioarXiv (2019). DOI.
[64] 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, 2019.
[63] 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, 2019. arXiv.
[62] M. Binder, S. Dandl and J. Moosbauer. “mosmafs: Multi-Objective Simultaneous Model and Feature Selection”. R package (2019).
[61] 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, 2019.
[60] 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, California, USA, 2019. arXiv.
[59] 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, 2019. arXiv.
[58] 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, 2019.
[57] 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, 2019.
[56] 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, 2019. DOI.
[55] 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.
[54] 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, 2019. DOI.
[53] 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 at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019. PDF.
[52] 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) abs/1905.10351 (2019). arXiv.
[51] 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, 2019. DOI.
[50] P. Gijsbers, E. LeDell, Thomas, S. Poirier, B. Bischl and J. Vanschoren. “An Open Source AutoML Benchmark”. In Proceedings of the 6th ICML Workshop on Automated Machine Learning (AutoML 2019). Long Beach, California, USA, 2019. arXiv.
[49] 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. “: Regression with Functional Data”. 2019. R-package.
[48] J. Golkov and D. Cremers. “Learning to Evolve”. Preprint at arXiv (2019). arXiv.
[47] 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 and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019. arXiv. URL.
[46] C. Happ, F. Scheipl, A. A. Gabriel and S. Greven. “A general framework for multivariate functional principal component analysis of amplitude and phase variation”. Stat 8.2 (2019). DOI.
[45] J. Held, A. Beer and T. Seidl. “Chain-detection Between Clusters”. Datenbank-Spektrum 19 (2019). DOI.
[44] J. Held, A. Beer and T. Seidl. “Chain-detection for DBSCAN”. In Proceedings of the 18th Symposium of Database Systems for Business, Technology and Web (BTW 2019). Rostock, Germany, 2019. DOI.
[43] M. Hunemörder, D. Kazempour, 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 2019). Berlin, Germany, 2019. PDF.
[42] 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, 2019. PDF.
[41] 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). Orlando, Florida, USA, 2019. URL.
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[38] 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, 2019. PDF.
[37] 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, 2019. PDF.
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[33] 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, 2019. PDF.
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[31] 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, 2019. arXiv.
[30] 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 2019). Munich, Germany, 2019.
[29] M. Lang, M. Binder, J. Richter, P. Schratz, F. Pfisterer, S. Coors, Q. A. Q. A., G. Casalicchio, L. Kotthoff and B. Bischl. “mlr3: A modern object-oriented machine learning framework in R”. The Journal of Open Source Software 4.44 (2019). DOI.
[28] M. Lotfollahi, F. A. Wolf and F. J. Theis. “scGen predicts single-cell perturbation responses”. Nature Methods 16.8 (2019). DOI.
[27] 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 (ICDMW 2019). Beijing, China, 2019. arXiv.
[26] 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, 2019. URL.
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[23] M. Moeller, T. Möllenhoff and D. Cremers. “Controlling Neural Networks via Energy Dissipation”. In Proceedings of the 13th International Conference on Computer Vision (ICCV 2019). Seoul, Korea, 2019.
[22] C. Molnar, G. Casalicchio and B. Bischl. “Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability”. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019. arXiv. URL.
[21] 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 2019). Amsterdam, Netherlands, 2019. DOI.
[20] F. Pfisterer, L. Beggel, X. Sun, F. Scheipl and B. Bischl. “Benchmarking time series classification -- Functional data vs machine learning approaches”. Preprint at arXiv (2019). arXiv.
[19] F. Pfisterer, S. Coors, J. Thomas and B. Bischl. “Multi-Objective Automatic Machine Learning with AutoxgboostMC”. Paper presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019. arXiv.
[18] F. Pfisterer, J. Thomas and B. Bischl. “Towards Human Centered AutoML”. Preprint at arXiv (2019). arXiv.
[17] 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.
[16] 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 2019). Vienna, Austria, 2019. DOI.
[15] C. A. Scholbeck, C. Molnar, C. Heumann, B. Bischl and G. Casalicchio. “Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations”. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019. arXiv.
[14] Y. Shen, T. Wu, C. Domokos and D. Cremers. “Probabilistic Discriminative Learning with Layered Graphical Models”. Preprint at arXiv (2019). arXiv.
[13] X. Sun, A. Bommert, F. Pfisterer, J. Rahenfürher, M. Lang and B. Bischl. “High dimensional restrictive federated model selection with multi-objective bayesian optimization over shifted distributions”. In Proceedings of the Intelligent Systems Conference 2019 ((IntelliSys 2019). London, UK, 2019.
[12] X. Sun, J. Lin and B. Bischl. “ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning”. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019.
[11] F. A. Wolf, F. K. Hamey, M. Plass, J. Solana, J. 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

[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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[8] 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 bioarXiv (2018). DOI.
[7] J. Minkwitz, F. Scheipl, E. Binder, C. Sander, U. Hegerl and H. Himmerich. “Generalised functional additive models for brain arousal state dynamics (Poster)”. In Proceedings of the 20th International Pharmaco-EEG Society for Preclinical and Clinical Electrophysiological Brain Research Meeting (IPEG 2018). Zurich, Switzerland, 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.