Home >> Publications

Publications by our Members

2023

[611]
D. Bär, F. Calderon, M. Lawlor, S. Licklederer, M. Totzauer and S. Feuerriegel.
Analyzing Social Media Activities at Bellingcat.
15th ACM Web Science Conference 2023 (WebSci 2023). Austin, TX, USA, 2023-04-30/2023-05-01. DOI.
[610]
D. Bär, N. Pröllochs and S. Feuerriegel.
Finding Qs: Profiling QAnon Supporters on Parler.
17th International AAAI Conference on Web and Social Media (ICWSM 2023). Limassol, Cyprus, 2023-06-05/2023-06-08. arXiv.
[609]
L. G. M. Bauer, C. Leiber, C. Böhm and C. Plant.
Extension of the Dip-test Repertoire - Efficient and Differentiable p-value Calculation for Clustering.
SIAM International Conference on Data Mining (SDM 2023). Minneapolis, MN, USA, 2023-04-27/2023-04-29. DOI.
[608]
M. Biloš, K. Rasul, A. Schneider, Y. Nevmyvaka and S. Günnemann.
Modeling Temporal Data as Continuous Functions with Process Diffusion.
40th International Conference on Machine Learning (ICML 2023). Honolulu, Hawaii, 2023-07-23/2023-07-29. arXiv.
[607]
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.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (2023). DOI.
[606]
V. Blaschke, H. Schütze and B. Plank.
A Survey of Corpora for Germanic Low-Resource Languages and Dialects.
24th Nordic Conference on Computational Linguistics (NoDaLiDa 2023). Tórshavn, Faroe Islands, 2023-05-22/2023-05-24. arXiv.
[605]
V. Blaschke, H. Schütze and B. Plank.
Does Manipulating Tokenization Aid Cross-Lingual Transfer? A Study on POS Tagging for Non-Standardized Languages.
10th Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023) at the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023). Dubrovnik, Croatia, 2023. PDF.
[604]
A. Bokhovkin and A. Dai.
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada, 2023-06-18/2023-06-23. URL.
[603]
M. Bonafini, M. Fornasier and B. Schmitzer.
Data-driven entropic spatially inhomogeneous evolutionary games.
European Journal of Applied Mathematics 34.1 (2023). DOI.
[602]
B. Bonnet, C. Cipriani, M. Fornasier and H. Huang.
A measure theoretical approach to the mean-field maximum principle for training NeurODEs.
Nonlinear Analysis 227 (2023). DOI.
[601]
A. Chronopoulou, D. Stojanovski and A. Fraser.
Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation.
6th Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023) at the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023). Dubrovnik, Croatia, 2023-05-02/2023-05-06. arXiv.
[600]
M. Eisenberger, A. Tokerand, L. Leal-Taixé and D. Cremers.
G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada, 2023-06-18/2023-06-23. arXiv.
[599]
Y. Elazar, N. Kassner, S. Ravfogel, A. Feder, A. Ravichander, M. Mosbach, Y. Belinkov, H. Schütze and Y. Goldberg.
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions.
Preprint at arXiv (2023). arXiv.
[598]
A. Farshad, Y. Yeganeh and N. Navab.
Learning to learn in medical applications: A journey through optimization.
Meta-Learning with Medical Imaging and Health Informatics Applications. The MICCAI Society book Series (2023). DOI.
[597]
M. Feurer, K. Eggensperger, E. Bergman, F. Pfisterer, B. Bischl and F. Hutter.
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives.
21st International Symposium on Intelligent Data Analysis (IDA 2023). Louvain-la-Neuve, Belgium, 2023-04-12/2023-04-14. DOI.
[596]
D. Frauen and S. Feuerriegel.
Estimating individual treatment effects under unobserved confounding using binary instruments.
11th International Conference on Learning Representations (ICLR 2023). Kigali, Rwanda, 2023-05-01/2023-05-05. arXiv.
[595]
D. Frauen, T. Hatt, V. Melnychuk and S. Feuerriegel.
Estimating Average Causal Effects from Patient Trajectories.
37th Conference on Artificial Intelligence (AAAI 2023). Washington, DC, USA, 2023-02-07/2023-02-14. arXiv.
[594]
C. Fritz, G. De Nicola, S. Kevork, D. Harhoff and G. Kauermann.
Modelling the large and dynamically growing bipartite network of German patents and inventors.
Journal of the Royal Statistical Society. Series A (Statistics in Society) OnlineFirst (2023). DOI.
[593]
A. Giovagnoli, Y. Ma, M. Schubert and V. Tresp.
QNEAT: Natural Evolution of Variational Quantum Circuit Architecture.
Genetic and Evolutionary Computation Conference (GECCO 2023). Lisbon, Portugal, 2023-07-15/2023-07-19. arXiv.
[592]
J. W. Grootjen, H. Weingärtner and S. Mayer.
Highlighting the Challenges of Blinks in Eye Tracking for Interactive Systems.
8th International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (PETMEI 2023) at the ACM Symposium on Eye Tracking Research and Applications (ETRA 2023). Tübingen, Germany, 2023-05-30/2023-06-02. DOI.
[591]
L. Haliburton, N. Bartłomiejczyk, A. Schmidt, P. W. Woźniak and J. Niess.
The Walking Talking Stick: Understanding Automated Note-Taking in Walking Meetings.
Conference on Human Factors in Computing Systems (CHI 2023). Hamburg, Germany, 2023-04-23/2023-04-28. DOI.
[590]
L. Haliburton, S. Ghebremedhin, R. Welsch, A. Schmidt and Mayer.
Investigating Labeler Bias in Face Annotation for Machine Learning.
Preprint at arXiv (2023). arXiv.
[589]
L. Haliburton, S. Kheirinejad, A. Schmidt and S. Mayer.
Exploring Smart Standing Desks to Foster a Healthier Workplace.
ACM Conference on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT 2023). Cancun, Mexico, 2023-10-08/2023-10-12. DOI.
[588]
K. Hämmerl, B. Deiseroth, P. Schramowski, J. Libovický, C. A. Rothkopf, A. Fraser and K. Kersting.
Speaking Multiple Languages Affects the Moral Bias of Language Models.
Findings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023). Toronto, Canada, 2023-07-09/2023-07-14. arXiv.
[587]
Z. Han, R. Liao, J. Gu, Y. Zhang, Z. Ding, Y. Gu, H. Köppl, H. Schütze and V. Tresp.
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations.
Findings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023). Toronto, Canada, 2023-07-09/2023-07-14, to appear. arXiv.
[586]
L. Härenstam-Nielsen, N. Zeller and D. Cremers.
Semidefinite Relaxations for Robust Multiview Triangulation.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada, 2023-06-18/2023-06-23. arXiv.
[585]
P. Heid.
A short note on an adaptive damped Newton method for strongly monotone and Lipschitz continuous operator equations.
Archiv der Mathematik (2023). DOI.
[584]
G. Heinze, A.-L. Boulesteix, M. Kammer, T. P. Morris, I. R. White and the Simulation Panel of the STRATOS initiative the Simulation Panel of the STRATOS initiative.
Phases of methodological research in biostatistics—Building the evidence base for new methods.
Biometrical Journal (2023). DOI.
[583]
M. Herrmann, F. Pfisterer and F. Scheipl.
A geometric framework for outlier detection in high-dimensional data.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery e1491 (2023). DOI.
[582]
R. Koner, T. Hannan, S. Shit, S. Sharifzadeh, M. Schubert, T. Seidl and V. Tresp.
InstanceFormer: An online Video Instance Segmentation Framework.
37th Conference on Artificial Intelligence (AAAI 2023). Washington, DC, USA, 2023-02-07/2023-02-14. arXiv.
GitHub.
[581]
G. König, T. Freiesleben and M. Grosse-Wentrup.
Improvement-focused causal recourse (ICR).
37th Conference on Artificial Intelligence (AAAI 2023). Washington, DC, USA, 2023-02-07/2023-02-14. arXiv.
[580]
J. Kostin, F. Krahmer and D. Stöger.
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?.
Preprint at arXiv (2023). arXiv.
[579]
C. Luther, G. König and M. Grosse-Wentrup.
Efficient SAGE Estimation via Causal Structure Learning.
26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023). Valencia, Spain, 2023-04-25/2023-04-27. URL.
[578]
Y. Mansour and R. Heckel.
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada, 2023-06-18/2023-06-23. URL.
[577]
C. Molnar, G. König, B. Bischl and G. Casalicchio.
Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach.
Data Mining and Knowledge Discovery (2023). DOI.
[576]
J. Moosbauer, G. Casalicchio, M. Lindauer and B. Bischl.
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution.
Workshop on Configuration and Selection of Algorithms (COSEAL 2023). Paris, France, 2023-03-06/2023-03-08. arXiv.
[575]
T. Nagler.
Statistical Foundations of Prior-Data Fitted Networks.
40th International Conference on Machine Learning (ICML 2023). Honolulu, Hawaii, 2023-07-23/2023-07-29. PDF.
[574]
E. Nie, S. Liang, H. Schmid and H. Schütze.
Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages.
In Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023). Toronto, Canada, 2023-07-09/2023-07-14. arXiv.
[573]
C. Nießl, S. Hoffmann, T. Ullmann and A.-L. Boulesteix.
Explaining the optimistic performance evaluation of newly proposed methods: A cross-design validation experiment.
Biometrical Journal (2023). DOI.
[572]
R. Paolino, A. Bojchevski, S. Günnemann, G. Kutyniok and R. Levie.
Unveiling the Sampling Density in Non-Uniform Geometric Graphs.
11th International Conference on Learning Representations (ICLR 2023). Kigali, Rwanda, 2023-05-01/2023-05-05. arXiv.
[571]
M. Ploner, A. Buyx, J. Gempt, J. Gjorgjieva, R. Müller, J. Priller, D. Rückert, B. Wolfrum and S. N. Jacob.
Reengineering neurotechnology: placing patients first.
Nature Mental Health 1 (2023). DOI.
[570]
M. Rezaei, A. Vahidi, T. Elze, B. Bischl and M. Eslami.
Self-supervised Learning and Self-labeling Framework for Glaucoma Detection.
Investigative Ophthalmology and Visual Science 64.8 (2023), to appear.
[569]
A. Roy Guha, S. Siddiqui, S. Pölsterl, A. Farshad, N. Navab and C. Wachinger.
Few-shot segmentation of 3D medical images.
Meta-Learning with Medical Imaging and Health Informatics Applications. The MICCAI Society book Series (2023). DOI.
[568]
D. Rügamer, C. Kolb and N. Klein.
Semi-Structured Distributional Regression.
American Statistician (2023). DOI.
[567]
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.
Journal of Statistical Software (2023). DOI.
[566]
A. Scagliotti.
Optimal control of ensembles of dynamical systems.
ESAIM - Control, Optimisation and Calculus of Variations 29.22 (2023). DOI.
[565]
S. Schäfer, D. F. Henning and S. Leutenegger.
GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation and Tracking in the Wild.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023). Detroit, MI, USA, 2023-10-01/2023-10-05, submitted.
[564]
L. Schneider, B. Bischl and J. Thomas.
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models.
Genetic and Evolutionary Computation Conference (GECCO 2023). Lisbon, Portugal, 2023-07-15/2023-07-19, to appear.
[563]
J. Seidenschwarz, G. Braso, I. Elezi and L. Leal-Taixé.
Simple Cues Lead to a Strong Multi-Object Tracker.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada, 2023-06-18/2023-06-23. arXiv.
[562]
N. Tathawadekar, N. A. K Doan, C. F. Silva and N. Thuerey.
Hybrid neural network pde solvers for reacting flows.
11th International Conference on Learning Representations (ICLR 2023). Kigali, Rwanda, 2023-05-01/2023-05-05. arXiv.
[561]
Ladner, Tobias and Althoff, Matthias.
Automatic Abstraction Refinement in Neural Network Verification Using Sensitivity Analysis.
26th ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2023). San Antonio, TX, USA, 2023-05-09/2023-05-12. DOI.
[560]
T. Ullmann, S. Peschel, P. Finger, C. Müller and A.-L. Boulesteix.
Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.
PLOS Computational Biology (2023). DOI.
[559]
T. Weber, M. Ingrisch, B. Bischl and D. Rügamer.
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.
27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023). Osaka, Japan, 2023-05-25/2023-05-28. DOI.
[558]
H. Weerts, F. Pfisterer, M. Feurer, K. Eggensperger, E. Bergman, N. Awad, J. Vanschoren, M. Pechenizkiy, B. Bischl and F. Hutter.
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML.
Preprint at arXiv (2023). arXiv.
[557]
L. Weissweiler, T. He, N. Otani, D. R. Mortensen, L. Levin and H. Schütze.
Construction Grammar Provides Unique Insight into Neural Language Models.
Georgetown University Round Table on Linguistics (GURT 2023). Washington D.C., USA, 2023-03-09/2023-03-12.
[556]
M. Windl, A. Schmidt and S. S. Feger.
Investigating Tangible Privacy-Preserving Mechanisms for Future Smart Homes.
Conference on Human Factors in Computing Systems (CHI 2023). Hamburg, Germany, 2023-04-23/2023-04-28. DOI.
[555]
M. Windl, V. Winterhalter, A. Schmidt and S. Mayer.
Understanding and Mitigating Technology-Facilitated Privacy Violations in the Physical World.
Conference on Human Factors in Computing Systems (CHI 2023). Hamburg, Germany, 2023-04-23/2023-04-28. DOI.
[554]
D. Winkel, N. Strauß, M. Schubert, Y. Ma and T. Seidl.
Constrained Portfolio Management using Action Space Decomposition for Reinforcement Learning.
27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023). Osaka, Japan, 2023-05-25/2023-05-28. DOI.
[553]
Y. Zhang, Y. Ma, T. Seidl and V. Tresp.
Adaptive Multi-Resolution Attention with Linear Complexity.
International Joint Conference on Neural Networks (IJCNN 2023). Gold Coast Convention and Exhibition Centre, Queensland, Australia, 2023-07-18/2023-07-23. arXiv.
[552]
L. Zumeta-Olaskoaga, M. Weigert, J. Larruskain, E. Bikandi, J. Setuain, J. Lekue, H. Küchenhoff and D. J. Lee.
Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models.
Advances in Statistical Analysis 107 (2023). DOI.
[551]
X. Zuo, N. Yang, N. Merrill, B. Xu and S. Leutenegger.
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion.
IEEE Robotics and Automation Letters (2023). DOI.


2022

[550]
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, 2022-07-23/2022-07-29. DOI.
[549]
D. Alivanistos, M. Berrendorf, M. Cochez and M. Galkin.
Query Embedding on Hyper-Relational Knowledge Graphs.
10th International Conference on Learning Representations (ICLR 2022). Virtual, 2022-04-25/2022-04-29. URL.
GitHub.
[548]
Q. Au, J. Herbinger, C. Stachl, B. Bischl and G. Casalicchio.
Grouped Feature Importance and Combined Features Effect Plot.
Data Mining and Knowledge Discovery (2022). arXiv.
[547]
D. Azinović, O. Maury, C. Hery, M. Nießner and J. Thies.
High-Res Facial Appearance Capture from Polarized Smartphone Images.
Preprint at arXiv (2022). arXiv.
[546]
J. Baan, W. Aziz, B. Plank and R. Fernandez.
Stop Measuring Calibration When Humans Disagree.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[545]
R. Bach and F. Kreuter.
Big Data in einer digitalisierten, datengestützten Demokratie.
Demokratie und Öffentlichkeit im 21. Jahrhundert – zur Macht des Digitalen (2022). DOI.
[544]
S. Bahmani, O. Hahn, E. Zamfir, N. Araslanov, D. Cremers and S. Roth.
Semantic Self-adaptation: Enhancing Generalization with a Single Sample.
Preprint at arXiv (2022). arXiv.
[543]
S. Bähr, G.-C. Haas, F. Keusch, F. Kreuter and M. Trappmann.
Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data.
Social Science Computer Review 40.1 (2022). DOI.
[542]
A. Balogh, A. Harman and F. Kreuter.
Real-Time Analysis of Predictors of COVID-19 Infection Spread in Countries in the European Union Through a New Tool.
International Journal of Public Health 67 (2022). DOI.
[541]
E. Bassignana, M. Müller-Eberstein, M. Zhang and B. Plank.
Evidence > Intuition: Transferability Estimation for Encoder Selection.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[540]
E. Bassignana and B. Plank.
CrossRE: A Cross-Domain Dataset for Relation Extraction.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[539]
A. Bauer, M. Weigert and H. Jalal.
APCtools: Descriptive and Model-based Age-Period-Cohort Analysis.
The Journal of Open Source Software 7.73 (2022). DOI.
[538]
G. Beaudry, O. Drouin, J. Gravel, A. Smyrnova, A. Bender, M. Orri, M.-C. Geoffroy and N. .
A comparative analysis of pediatric mental health-related emergency department utilization in Montréal, Canada, before and during the COVID-19 pandemic.
Annals of General Psychiatry 17 (2022). DOI.
[537]
A. S. Becker-Pennrich, M. M. Mandl, C. Rieder, D. J. Hoechter, K. Dietz, B. P. Geisler, A.-L- Boulesteix, R. Tomasi and L. C. Hinske.
Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy.
Preprint at medRxiv (2022). DOI.
[536]
V. Bengs, E. Hüllermeier and W. Waegeman.
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[535]
V. Bengs, A. Saha and E. Hüllermeier.
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models.
39th International Conference on Machine Learning (ICML 2022). Baltimore, MD, USA, 2022-07-17/2022-07-23. URL.
[534]
U. Berger, C. Fritz and G. Kauermann.
Reihentestungen an Schulen können die Dunkelziffer von COVID-19 Infektionen unter Schülern signifikant senken.
Das Gesundheitswesen 84.06 (2022). DOI.
[533]
M. Bernhard and M. Schubert.
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations.
30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2022). Seattle, WA, USA, 2022-11-01/2022-11-04. DOI.
GitHub.
[532]
A. Blattmann, R. Rombach, K. Oktay and B. Ommer.
Retrieval-Augmented Diffusion Models.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[531]
F. Bongratz, A.-M. Rickmann, S. Pölsterl and C. Wachinger.
Vox2cortex: fast explicit reconstruction of cortical surfaces from 3D MRI scans with geometric deep neural networks.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[530]
L. Bothmann, K. Peters and B. Bischl.
What Is Fairness? Implications For FairML.
Preprint at arXiv (2022). arXiv.
[529]
J. Brandt, V. Bengs, B. Haddenhorst and E. Hüllermeier.
Finding optimal arms in non-stochastic combinatorial bandits with semi-bandit feedback and finite budget.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[528]
G. Brasó, O. Cetintas and L. Leal-Taixé.
Multi-Object Tracking and Segmentation Via Neural Message Passing.
International Journal of Computer Vision 130.12 (2022). DOI.
[527]
C. Brunner, A. Duensing, C. Schröder, M. Mittermair, V. Golkov, M. Pollanka, D. Cremers and R. Kienberger.
Deep Learning in Attosecond Metrology.
Optics Express 30.9 (2022), Editor's Pick. DOI.
[526]
M. Brunner, P. Heid, M. Innerberger, A. Miraci, D. Praetorius and J. Streitberger.
Adaptive FEM with quasi-optimal overall cost for nonsymmetric linear elliptic PDEs.
Preprint at arXiv (2022). arXiv.
[525]
A. Caelles, T. Meinhardt, G. Brasó and L. Leal-Taixé.
DeVIS: Making Deformable Transformers Work for Video Instance Segmentation.
Preprint at arXiv (2022). arXiv.
[524]
A. Campagner, J. Lienen, E. Hüllermeier and D. Ciucci.
Scikit-Weak: A Python Library for Weakly Supervised Machine Learning.
International Joint Conference on Rough Sets (IJCRS 2022). Suzhou, China, 2022-11-11/2022-11-14. DOI.
[523]
Q. Cheng, N. Zeller and D. Cremers.
Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications.
Preprint at arXiv (2022). arXiv.
[522]
S. Dandl, A. Bender and T. Hothorn.
Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests.
Preprint at arXiv (2022). arXiv.
[521]
S. Dandl, T. Hothorn, H. Seibold, E. Sverdrup, S. Wager and A. Zeileis.
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?.
Preprint at arXiv (2022). arXiv.
[520]
S. Dandl, F. Pfisterer and B. Bischl.
Multi-Objective Counterfactual Fairness.
Genetic and Evolutionary Computation Conference (GECCO 2022). Boston, MA, USA, 2022-07-09/2022-07-13. DOI.
[519]
D. Das, Q. Khan and D. Cremers.
Ventriloquist-Net: Leveraging Speech Cues for Emotive Talking Head Generation.
IEEE International Conference on Image Processing (ICIP 2022). Bordeaux, France, 2022-10-16/2022-10-19. DOI.
[518]
P. Dendorfer, V. Yugay, A. Ošep and L. Leal-Taixé.
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?.
Preprint at arXiv (2022). arXiv.
[517]
D. Deng, F. Karl, F. Hutter, B. Bischl and M. Lindauer.
Efficient Automated Deep Learning for Time Series Forecasting.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022-09-19/2022-09-22. DOI.
[516]
G. De Nicola, B. Sischka and G. Kauermann.
Mixture Models and Networks: The Stochastic Block Model.
Statistical Modelling 22.1-2 (2022). DOI.
[515]
C. Diller, T. Funkhouser and A. Dai.
Forecasting characteristic 3D poses of human actions.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[514]
Z. Ding, R. Qi, Z. Li, B. He, J. Wu, Y. Ma, Z. Meng, Z. Han and V. Tresp.
Forecasting Question Answering over Temporal Knowledge Graphs.
Preprint at arXiv (2022). arXiv.
[513]
Z. Ding, J. Wu, B. He, Y. Ma, Z. Han and V. Tresp.
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information.
4th Conference on Automated Knowledge Base Construction (AKBC 2022). London, UK, 2022-11-03/2022-11-05. PDF.
[512]
E. Dorigatti, B. Bischl and B. Schubert.
Improved proteasomal cleavage prediction with positive-unlabeled learning.
Preprint at arXiv (2022). arXiv.
[511]
E. Dorigatti, J. Goschenhofer, B. Schubert, M. Rezaei and B. Bischl.
Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection.
Preprint at arXiv (2022). arXiv.
[510]
E. Dorigatti, J. Schweisthal, B. Bischl and M. Rezaei.
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision.
Preprint at arXiv (2022). arXiv.
[509]
W. Durani, D. Mautz, C. Plant and C. Böhm.
DBHD: Density-based clustering for highly varying density.
22nd IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL, USA, 2022-11-30/2022-12-02. DOI.
[508]
M. Eisenberger, A. Toker, L. Leal-Taixé, F. Bernard and D. Cremers.
A Unified Framework for Implicit Sinkhorn Differentiation.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[507]
I. Elezi, J. Seidenschwarz, L. Wagner, S. Vascon, A. Torcinovich, M. Pelillo and L. Leal-Taixé.
The Group Loss++: A deeper look into group loss for deep metric learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022). DOI.
[506]
I. Elezi, Z. Yu, A. Anandkumar, L. Leal-Taixé and J. M. Alvarez.
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[505]
P. Engstler, M. Keicher, D. Schinz, K. Mach, A. S. Gersing, S. C. Foreman, S. S. Goller, J. Weissinger, J. Rischewski, A.-S. Dietrich, B. Wiestler, J. S. Kirschke, A. Khakzar and N. Navab.
Interpretable Vertebral Fracture Diagnosis.
Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2022) at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022-09-18/2022-09-22. DOI.
[504]
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.
[503]
A. Farshad, A. Makarevich, V. Belagiannis and N. Navab.
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation.
4th Workshop on Domain Adaptation and Representation Transfer (DART 2022) at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022-09-18/2022-09-22. DOI.
[502]
A. Farshad, Y. Yeganeh, H. Dhamo, F. Tombari and N. Navab.
DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation.
33rd British Machine Vision Conference (BMVC 2022). London, UK, 2022-11-21/2022-11-24. URL.
[501]
A. Farshad, Y. Yeganeh, P. Gehlbach and N. Navab.
Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation.
25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022-09-18/2022-09-22. DOI.
[500]
B. Felderer, A. Birg and F. Kreuter.
Paradaten.
Handbuch Methoden der empirischen Sozialforschung (2022). DOI.
[499]
A. Fernández-Fontelo, F. Henninger, P. J. Kieslich, F. Kreuter and S. Greven.
Classification ensembles for multivariate functional data with application to mouse movements in web surveys.
Preprint at arXiv (2022). arXiv.
[498]
A. Fiske, S. McLennan and A. Buyx.
Qualitative Evidence for Concern: Digital Health Technologies and the COVID-19 Pandemic.
AJOB Neuroscience 13.3 (2022). DOI.
[497]
V. Fomenko, I. Elezi, D. Ramanan, L. Leal-Taixé and A. Ošep.
Learning to Discover and Detect Objects.
Preprint at arXiv (2022). arXiv.
[496]
M. Fornasier, H. Huang, L. Pareschi and P. Sünnen.
Anisotropic Diffusion in Consensus-Based Optimization on the Sphere.
SIAM Journal on Optimization 32.3 (2022). DOI.
[495]
M. Fornasier, T. Klock and K. Riedl.
Convergence of Anisotropic Consensus-Based Optimization in Mean-Field Law.
25th European Conference on the Applications of Evolutionary Computation (EvoApplications 2022), Held as Part of EvoStar 2022. Madrid, Spain, 2022-04-20/2022-04-22. DOI.
[494]
R. Foygel Barber, M. Drton, N. Sturma and L. Weihs.
Half-trek criterion for identifiability of latent variable models.
Annals of Statistics 50.6 (2022). DOI.
[493]
T. Freiesleben, G. König, C. Molnar and A. Tejero-Cantero.
Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena.
Preprint at arXiv (2022). arXiv.
[492]
C. Frey, Y. Ma and M. Schubert.
SEA: Graph Shell Attention in Graph Neural Networks.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022-09-19/2022-09-22. DOI.
[491]
C. Frey and M. Schubert.
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs.
Preprint at arXiv (2022). arXiv.
[490]
C. Fritz, G. De Nicola, F. Günther, D. Rügamer, M. Rave, M. Schneble, A. Bender, M. Weigert, R. Brinks, A. Hoyer, U. Berger, H Küchenhoff and G. Kauermann.
Challenges in Interpreting Epidemiological Surveillance Data – Experiences from Germany.
Journal of Computational and Graphical Statistics (2022). DOI.
[489]
C. Fritz, G. De Nicola, M. Rave, M. Weigert, Y. Khazaei, U. Berger, H. Küchenhoff and G. Kauermann.
Statistical modelling of COVID-19 data: Putting generalized additive models to work.
Statistical Modelling (2022). DOI.
[488]
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). DOI.
[487]
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). DOI.
[486]
C. Fritz, M. Mehrl, P. W. Thurner and G. Kauermann.
All that Glitters is not Gold: Relational Events Models with Spurious Events.
Network Science (2022). DOI.
[485]
M. Fromm.
Machine learning driven argument mining.
. DOI.
[484]
M. Fromm, M. Berrendorf, J. Reiml, I. Mayerhofer, S. Bhargava, E. Faerman and T. Seidl.
Towards a Holistic View on Argument Quality Prediction.
Preprint at arXiv (2022). arXiv.
[483]
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.
6th ACL Workshop on Structured Prediction for NLP (SPNLP 2022) at the In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022). Dublin, Ireland, 2022-05-22/2022-05-27. DOI.
[482]
M. Galkin, M. Berrendorf and C. T. Hoyt.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs.
Workshop on Graph Learning Benchmarks (GLB 2022) at the International World Wide Web Conference (WWW 2022). Virtual, 2022-04-22/2022-04-29. arXiv.
GitHub.
[481]
V. Gangadharan, H. Zheng, F. J. Taberner, J. Landry, T. A. Nees, J. Pistolic, N. Agarwal, D. Männich, V. Benes, M. Helmstaedter, B. Ommer, S. G. Lechner, T. Kuner and R. Kuner.
Neuropathic pain caused by miswiring and abnormal end organ targeting.
Nature 606 (2022). URL.
[480]
C. Geldhauser, J. Liljegren and P. Nordqvist.
All's well that FID's well? Result quality and metric scores in GAN models for lip-sychronization tasks.
Preprint at arXiv (2022). arXiv.
[479]
F. Gerdon, R. L. Bach, C. Kern and F. Kreuter.
Social impacts of algorithmic decision-making: A research agenda for the social sciences.
Big Data and Society 9.1 (2022). DOI.
[478]
P. Gijsbers, M. L. P. Bueno, S. Coors, E. LeDell, S. Poirier, J. Thomas, B. Bischl and J. Vanschoren.
AMLB: an AutoML Benchmark.
Journal of Machine Learning Research (accepted) (2022). arXiv.
[477]
S. Gilhuber, M. Berrendorf, Y. Ma and T. Seidl.
Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations.
6th International Workshop on Interactive Adaptive Learning (IAL 2022) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022-09-19/2022-09-22. PDF.
GitHub.
[476]
S. Gilhuber, P. Jahn, Y. Ma and T. Seidl.
Verips: Verified Pseudo-label Selection for Deep Active Learning.
22nd IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL, USA, 2022-11-30/2022-12-02. DOI.
GitHub.
[475]
M. Gladkova, N. Korobov, N. Demmel, A. Ošep, L. Leal-Taixé and D. Cremers.
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022). Kyoto, Japan, 2022-10-23/2022-10-27. DOI.
[474]
J. Goldsmith and F. Scheipl.
tf: S3 classes and methods for tidy functional data. R package.
2022. GitHub.
[473]
J. Goldsmith and F. Scheipl.
tidyfun: Clean, wholesome, tidy fun with functional data in R. R package.
2022. GitHub.
[472]
P. Grassal, M. Prinzler, T. Leistner, C. Rother, M. Nießner and J. Thies.
Neural Head Avatars from Monocular RGB Videos.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[471]
F. Günther, S. Einhauser, D. Peterhoff, S. Wiegrebe, H. H. Niller, S. Beileke, P. Steininger, R. Burkhardt, H. Küchenhoff, O. Gefeller, K. Überla, I. M. Heid and R. Wagner.
Higher Infection Risk among Health Care Workers and Lower Risk among Smokers Persistent across SARS-CoV-2 Waves–Longitudinal Results from the Population-Based TiKoCo Seroprevalence Study.
International Journal of Environmental Research and Public Health 19.24 (2022). DOI.
[470]
L. Hang, Q. Khan, V. Tresp and D. Cremers.
Biologically Inspired Neural Path Finding.
15th International Conference on Brain Informatics (BI 2022). Padova, Italy, 2022-07-15/2022-07-15. DOI.
[469]
V. Hangya, H. S. Saadi and A. Fraser.
Improving Low-Resource Languages in Pre-Trained Multilingual Language Models.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[468]
T. Hannan, R. Koner, J. Kobold and M. Schubert.
Box Supervised Video Segmentation Proposal Network.
Preprint at arXiv (2022). arXiv.
[467]
W. Hartl, P. Kopper, A. Bender, F. Scheipl, A. G. Day, G. Elke and H. .
Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models.
Critical Care 26.7 (2022). DOI.
[466]
A. Hein, L. J. Meier, A. Buyx and K. Diepold.
A Fuzzy-Cognitive-Maps Approach to Decision-Making in Medical Ethics.
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022). Padua, Italy, 2022-07-18/2022-07-23. DOI.
[465]
F. Henninger, P. J. Kieslich, A. Fernández-Fontelo, S. Greven and F. Kreuter.
Privacy attitudes toward mouse-tracking paradata collection.
Preprint at SocArXiv (2022). DOI.
[464]
J. Herbinger, B. Bischl and G. Casalicchio.
REPID: Regional Effect Plots with implicit Interaction Detection.
25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022). Virtual, 2022-03-28/2022-03-30. URL.
[463]
M. Herrmann.
Towards more reliable machine learning: conceptual insights and practical approaches for unsupervised manifold learning and supervised benchmark studies.
. DOI.
[462]
M. Herrmann, D. Kazempour, F. Scheipl and P. Kröger.
Enhancing cluster analysis via topological manifold learning.
Preprint at arXiv (2022). arXiv.
[461]
L. Hetzel, S. Boehm, N. Kilbertus, S. Günnemann, M. Lotfollahi and F. J. Theis.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[460]
F. Hofherr, L. Koestler, F. Bernard and D. Cremers.
Neural Implicit Representations for Physical Parameter Inference from a Single Video.
Preprint at arXiv (2022). arXiv.
[459]
E. Hohma, C. Frey, A. Beer and T. Seidl.
SCAR - Spectral Clustering Accelerated and Robustified.
48th International Conference on Very Large Databases (VLDB 2022). Sydney, Australia (and hybrid), 2022-09-05/2022-09-09. DOI.
GitHub.
[458]
P. Holl, V. Koltun and N. Thuerey.
Scale-invariant Learning by Physics Inversion.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[457]
R. Hornung and A.-L. Boulesteix.
Interaction Forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects.
Computational Statistics and Data Analysis 171.107460 (2022). DOI.
[456]
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.
Workshop on Graph Learning Benchmarks (GLB 2022) at the International World Wide Web Conference (WWW 2022). Virtual, 2022-04-22/2022-04-29. arXiv.
[455]
H. H.-H. Hsu, Y. Shen and D. Cremers.
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs.
Workshop on New Frontiers in Graph Learning at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. URL.
[454]
H. H.-H. Hsu, Y. Shen, C. Tomani and D. Cremers.
What Makes Graph Neural Networks Miscalibrated?.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[453]
H. Huang, J. Qiu and K. Riedl.
Consensus-Based Optimization for Saddle Point Problems.
Preprint at arXiv (2022). arXiv.
[452]
H. Huang, J. Qiu and K. Riedl.
On the Global Convergence of Particle Swarm Optimization Methods.
Preprint at arXiv (2022). arXiv.
[451]
E. Hüllermeier, S. Destercke and M. H. Shaker.
Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison.
38th Conference on Uncertainty in Artificial Intelligence (UAI 2022). Eindhoven, Netherlands, 2022-08-02/2022-08-04. URL.
[450]
E. Hüllermeier, M. Wever, E. L. Mencica, J. Fürnkranz and M. Rapp.
A flexible class of dependence-aware multi-label loss functions.
Machine Learning 111.2 (2022). DOI.
[449]
N. Hurmer, X.-Y. To, M. Binder, H. A. Gündüz, P. C. Münch, R. Mreches, A. C. McHardy, B. Bischl and M. Rezaei.
Transformer Model for Genome Sequence Analysis.
Workshop on Learning Meaningful Representations of Life (LMRL 2022) at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. URL.
[448]
A. Imani, S. Severini, M. J. Sabet, F. Yvon and H. Schütze.
Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[447]
P. Kaiser, D. Rügamer and C. Kern.
Uncertainty as a key to fair data-driven decision making.
Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML 2022) at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. URL.
[446]
M. Keicher, K. Mullakaeva, T. Czempiel, K. Mach, A. Khakzar and N. Navab.
Few-shot Structured Radiology Report Generation Using Natural Language Prompts.
Preprint at arXiv (2022). arXiv.
[445]
C. Kern, F. Gerdon, R. L. Bach, F. Keusch and F. Kreuter.
Humans versus machines: Who is perceived to decide fairer? Experimental evidence on attitudes toward automated decision-making.
Patterns 3.10 (2022). DOI.
[444]
F. Keusch, S. Bähr, G.-C. Haas, F. Kreuter, M. Trappmann and S. Eckman.
Non-participation in smartphone data collection using research apps.
Journal of the Royal Statistical Society. Series A (Statistics in Society) (2022). DOI.
[443]
S. Kevork and G. Kauermann.
Bipartite Exponential Random Graph Models with Nodal Random Effects.
Social Networks 70 (2022). DOI.
[442]
S. Kevork and G. Kauermann.
Iterative Estimation of Mixed Exponential Random Graph Models with Nodal Random Effects.
Network Science 9.4 (2022). DOI.
[441]
A. Khakzar, P. Khorsandi, R. Nobahari and N. Navab.
Do Explanations Explain? Model Knows Best.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[440]
A. Kim, G. Brasó, A. Ošep and L. Leal-Taixé.
PolarMOT: How Far Can Geometric Relations Take us in 3D Multi-object Tracking?.
17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022-10-23/2022-10-27. DOI.
[439]
M. P. Kim, C. Kern, S. Goldwasser, F. Kreuter and O. Reingold.
Universal adaptability: Target-independent inference that competes with propensity scoring.
Proceedings of the National Academy of Sciences 119.4 (2022). DOI.
[438]
A. Klaß, S. M. Lorenz, M. W. Lauer-Schmaltz, D. Rügamer, B. Bischl, C. Mutschler and F. Ott.
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift.
Workshop on Spatio-Temporal Reasoning and Learning (STRL 2022) at the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022). Vienna, Austria, 2022-07-23/2022-07-29. URL.
[437]
S. Klenk, L. Koestler, D. Scaramuzza and D. Cremers.
E-NeRF: Neural Radiance Fields from a Moving Event Camera.
Preprint at arXiv (2022). arXiv.
[436]
P. Kocsis, P. Súkenı́k, G. Brasó, M. Nießner, L. Leal-Taixé and I. Elezi.
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes.
Preprint at arXiv (2022). arXiv.
[435]
L. Koestler, D. Grittner, M. Moeller, D. Cremers and Z. Lähner.
Intrinsic Neural Fields: Learning Functions on Manifolds.
17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022-10-23/2022-10-27. DOI.
[434]
A. Köksal, T. Schick and H. Schütze.
MEAL: Stable and Active Learning for Few-Shot Prompting.
Preprint at arXiv (2022). arXiv.
[433]
M. Kolmet, Q. Zhou, A. Ošep and L. Leal-Taixé.
Text2Pos: Text-to-Point-Cloud Cross-Modal Localization.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[432]
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.
26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022). Chengdu, China, 2022-05-16/2022-05-19. DOI.
[431]
L. Krombholz.
Multi-View Spectral Clustering on Single-View Data - Grouping High-Dimensional Representations of 3D Objects.
.
[430]
M. Kuppler, C. Kern, R. L. Bach and F. Kreuter.
From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making.
Frontiers in Sociology 7 (2022). DOI.
[429]
W. Lai, A. Chronopoulou and A. Fraser.
m4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[428]
J. Lane, B. Kim, F. Kreuter and A. Nunez.
The Value of Science: Special Theme.
Harvard Data Science Review 4.2 (2022). URL.
[427]
F. Lang, J. W. Grootjen, L. L. Chuang and T. Machulla.
IDeA: A Demonstration of a Mixed Reality System to Support Living with Central Field Loss.
Mensch und Computer (MuC 2022). Darmstadt, Germany, 2022-09-04/2022-09-07. DOI.
[426]
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.
[425]
S. Legler, T. Janjic, M. H. Shaker and E. Hüllermeier.
Machine learning for estimating parameters of a convective-scale model: A comparison of neural networks and random forests.
32nd Workshop of Computational Intelligence of the VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA). Berlin, Germany, 2022-12-01/2022-12-02. PDF.
[424]
C. Leiber, L. G. M. Bauer, M. Neumayr, C. Plant and C. Böhm.
The DipEncoder: Enforcing Multimodality in Autoencoders.
28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2022). Washington, DC, USA, 2022-08-14/2022-08-18. DOI.
[423]
C. Leiber, D. Mautz, C. Plant and C. Böhm.
Automatic Parameter Selection for Non-Redundant Clustering.
SIAM International Conference on Data Mining (SDM 2022). Virtual, 2022-04-28/2022-04-30. DOI.
[422]
Y. Li, A. Khakzar, Y. Zhang, M. Sanisoglu, S. T. Kim, M. Rezaei, B. Bischl and N. Navab.
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models.
2nd Workshop on Interpretable Machine Learning in Healthcare (IMLH 2022) at the the 39th International Conference on Machine Learning (ICML 2022). Baltimore, MD, USA, 2022-07-17/2022-07-23. arXiv.
[421]
T. Liu, Y. Liu, M. Hildebrandt, M. Joblin, H. Li and V. Tresp.
On Calibration of Graph Neural Networks for Node Classification.
International Joint Conference on Neural Networks (IJCNN 2022). Padua, Italy, 2022-07-18/2022-07-23. DOI.
[420]
Y. Liu, Y. Ma, M. Hildebrandt, M. Joblin and V. Tresp.
TLogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs.
36th Conference on Artificial Intelligence (AAAI 2022). Virtual, 2022-02-22/2022-03-01. DOI.
[419]
Y. Liu, S. Yan, L. Leal-Taixé, J. Hays and D. Ramanan.
Soft Augmentation for Image Classification.
Preprint at arXiv (2022). arXiv.
[418]
Y. Liu, I. E. Zulfikar, J. Luiten, A. Dave, D. Ramanan, B. Leibe, A. Ošep and L. Leal-Taixé.
Opening up Open World Tracking.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[417]
Z. Liu, Y. Ma, M. Schubert, Y. Ouyang and Z. Xiong.
Multi-Modal Contrastive Pre-training for Recommendation.
ACM International Conference on Multimedia Retrieval (ICMR 2022). Newark, NJ, USA, 2022-06-27/2022-06-30. DOI.
[416]
A. Lohrer, J. J. Binder and P. Kröger.
Group Anomaly Detection for Spatio-Temporal Collective Behaviour Scenarios in Smart Cities.
15th International Workshop on Computational Transportation Science (IWCTS 2022) at the 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2022). Seattle, WA, USA, 2022-11-01/2022-11-04. DOI.
[415]
M. Lotfollahi, M. Naghipourfar, M. D. Luecken, M. Khajavi, M. Büttner, M. Wagenstetter, Ž. Avsec, A. Gayoso, N. Yosef, M. Interlandi, S. Rybakov, A. V. Misharin and F. J. .
Mapping single-cell data to reference atlases by transfer learning.
Nature Biotechnology 40 (2022). DOI.
[414]
S. Malich, S. Bähr, G. C. Haas, F. Keusch, F. Kreuter and M. Trappmann.
Methodische Herausforderungen bei der Aufbereitung und Auswertung von Smartphone-Daten zur Messung sozialer Interaktion.
Frühjahrstagung 2022 der Sektion „Methoden der empirischen Sozialforschung“ der Deutschen Gesellschaft für Soziologie (DGS): Potentiale und Herausforderungen von digitalen Verhaltensdaten in der empirischen Sozialforschung. Virtual, 2022.
[413]
A. Markham, R. Das and M. Grosse-Wentrup.
A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations.
1st Conference on Causal Learning and Reasoning (CLeaR 2022). Eureka, CA, USA, 2022-04-11/2022-04-13. PDF.
[412]
A. Maronikolakis, P. Baader and H. Schütze.
Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes.
4th Workshop on Gender Bias in Natural Language Processing (GeBNLP 2022). Seattle, WA, USA, 2022-07-15. DOI.
[411]
A. Maronikolakis, A. Wisiorek, L. Nann, H. Jabbar, S. Udupa and H. Schütze.
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments.
Findings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022). Dublin, Ireland, 2022-05-22/2022-05-27. DOI.
[410]
S. McLennan, A. Meyer, K. Schreyer and A. Buyx.
German medical students´ views regarding artificial intelligence in medicine: A cross-sectional survey.
PLOS Digital Health (2022). DOI.
[409]
L. J. Meier, A. Hein, K. Diepold and A. Buyx.
Clinical Ethics – To Compute, or Not to Compute?.
American Journal of Bioethics 22.12 (2022). DOI.
[408]
T. Meinhardt, A. Kirillov, L. Leal-Taixé and C. Feichtenhofer.
Trackformer: Multi-object tracking with transformers.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[407]
L. Miklautz, M. Teuffenbach, P. Weber, R. Perjuci, W. Durani, C. Böhm and C. Plant.
Deep Clustering With Consensus Representations.
22nd IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL, USA, 2022-11-30/2022-12-02. DOI.
[406]
T. Milbich, K. Roth, B. Brattoli and B. Ommer.
Sharing Matters for Generalization in Deep Metric Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence 44.1 (2022). DOI.
[405]
M. Mittermeier, M. Weigert, D. Rügamer, H. Küchenhoff and R. Ludwig.
A deep learning based classification of atmospheric circulation types over Europe: projection of future changes in a CMIP6 large ensemble.
Environmental Research Letters 17 (2022). DOI.
[404]
M. Mittermeier, M. Weigert, D. Rügamer, H. Küchenhoff and R. Ludwig.
DeepECT: The Deep Embedded Cluster Tree.
Environmental Research Letters 17.8 (2022). DOI.
[403]
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.
IEEE Transactions on Evolutionary Computation 26.6 (2022). DOI.
[402]
J. Moosbauer, G. Casalicchio, M. Lindauer and B. Bischl.
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.
Preprint at arXiv (2022). arXiv.
[401]
T. Mortier, E. Hüllermeier, K. Dembczynski and W. Waegeman.
Set-valued prediction in hierarchical classification with constrained representation complexity.
38th Conference on Uncertainty in Artificial Intelligence (UAI 2022). Eindhoven, Netherlands, 2022-08-02/2022-08-04. URL.
[400]
D. Muhle, L. Koestler, N. Demmel, F. Bernard and D. Cremers.
The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[399]
F. Mujkanovic, S. Geisler, S. Günnemann and A. Bojchevski.
Are Defenses for Graph Neural Networks Robust?.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[398]
F. Müller, Q. Khan and D. Cremers.
Lateral Ego-Vehicle Control Without Supervision Using Point Clouds.
3rd International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2022). Paris, France, 2022-06-01/2022-06-03. DOI.
[397]
M. Müller-Eberstein, R. van der Goot and B. Plank.
Spectral Probing.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[396]
P. Mummoju, A. Wolff, M. Perdacher, C. Plant and C. Böhm.
Enhancing k-Means Algorithm with Tensor Processing Unit.
IEEE International Conference on Big Data (IEEE BigData 2022). Osaka, Japan, 2022-12-17/2022-12-20. DOI.
[395]
M. Narazani, I. Sarasua, S. Pölsterl, A. Lizarraga, I. Yakushev and C. Wachinger.
Is a PET All You Need? A Multi-modal Study for Alzheimer’s Disease Using 3D CNNs.
25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022-09-18/2022-09-22. DOI.
[394]
V. Nguyen, M. H. Shaker and E. Hüllermeier.
How to measure uncertainty in uncertainty sampling for active learning.
Machine Learning 111.1 (2022). DOI.
[393]
C. Nießl, M. Herrmann, C. Wiedemann, G. 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.
[392]
C. D. Nordeck, K. E. Riehm, E. J. Smail, C. Holingue, J. C. Kane, R. M. Johnson, C. B. Veldhuis, L. G. Kalb, E. A. Stuart, F. Kreuter and J. Thrul.
Changes in drinking days among United States adults during the COVID-19 pandemic.
Addiction 117.2 (2022). DOI.
[391]
Z. Nurlanov, D. Cremers and F. Bernard.
Efficient and Flexible Sublabel-Accurate Energy Minimization.
Preprint at arXiv (2022). arXiv.
[390]
I. Obadic, R. Roscher, D. A. Oliveira and X. Zhu.
Exploring Self-Attention for Crop-type Classification Explainability.
Preprint at arXiv (2022). arXiv.
[389]
F. Ott, N. L. Raichur, D. Rügamer, T. Feigl, H. Neumann, B. Bischl and C. Mutschler.
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression.
Preprint at arXiv (2022). arXiv.
[388]
F. Ott, D. Rügamer, L. Heublein, B. Bischl and C. Mutschler.
Cross-Modal Common Representation Learning with Triplet Loss Functions.
Preprint at arXiv (2022). arXiv.
[387]
F. Ott, D. Rügamer, L. Heublein, B. Bischl and C. Mutschler.
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift.
30th ACM International Conference on Multimedia (MM 2022). Lisbon, Portugal, 2022-10-10/2022-10-14. DOI.
[386]
F. Ott, D. Rügamer, L. Heublein, B. Bischl and C. Mutschler.
Joint Classification and Trajectory Regression of Online Handwriting Using a Multi-Task Learning Approach.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022). Waikoloa, Hawaii, 2022-01-04/2022-01-08. DOI.
[385]
F. Ott, D. Rügamer, L. Heublein, B. Bischl and C. Mutschler.
Representation Learning for Tablet and Paper Domain Adaptation in favor of Online Handwriting Recognition.
7th International Workshop on Multimodal pattern recognition of social signals in human computer interaction (MPRSS 2022) at the 26th International Conference on Pattern Recognition (ICPR 2022). Montreal, Canada, 2022-08-21/2022-08-25. arXiv.
[384]
F. Ott, D. Rügamer, L. Heublein, T. Hamann, J. Barth, B. Bischl and C. Mutschler.
Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens.
International Journal on Document Analysis and Recognition 25.4 (2022). DOI.
[383]
M. Ozgur Turkoglu, A. Becker, H. A. Gündüz, M. Rezaei, B. Bischl, R. Caye Daudt, S. D'Aronco, J. D. Wegner and K. Schindler.
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[382]
N. Peri, J. Luiten, M. Li, A. Ošep, L. Leal-Taixé and D. Ramanan.
Forecasting from LiDAR via Future Object Detection.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[381]
F. Pfisterer, L. Schneider, Moosbauer, M. Binder and B. Bischl.
YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization.
1st International Conference on Automated Machine Learning (AutoML-Conf 2022) co-located with the 39th International Conference on Machine Learning (ICML 2022). Baltimore, MD, USA, 2022-07-25/2022-07-27. URL.
GitHub.
[380]
B. Plank.
The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[379]
S. Pölsterl, C. Wachinger, Alzheimer's Disease Neuroimaging Initiative Alzheimer's Disease Neuroimaging Initiative and Japanese Alzheimer's Disease Neuroimaging Initiative Japanese Alzheimer's Disease Neuroimaging Initiative.
Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders.
Alzheimer's and Dementia (2022). DOI.
[378]
E. Pretzsch, V. Heinemann, S. Stintzing, A. Bender, S. Chen, J. W. Holch, F. O. Hofmann, H. Ren, F. Küchenhoff, J. Werner and Angele.
EMT-Related Genes Have No Prognostic Relevance in Metastatic Colorectal Cancer as Opposed to Stage II/III: Analysis of the Randomised, Phase III Trial FIRE-3 (AIO KRK 0306; FIRE-3).
Cancers 14.22 (2022). DOI.
[377]
J. Ramjith, A. Bender, K. C. B. Roes and M. A. Jonker.
Recurrent events analysis with piece-wise exponential additive mixed models.
Statistical Modelling (2022). DOI.
[376]
K. Rath, D. Rügamer, B. Bischl, U. von Toussaint, C. Rea, A. Maris, R. Granetz and C. Albert.
Data augmentation for disruption prediction via robust surrogate models.
Journal of Plasma Physics 88.5 (2022). DOI.
[375]
M. Rezaei, E. Dorigatti, D. Rügamer and B. Bischl.
Learning Statistical Representation with Joint Deep Embedded Clustering.
IEEE International Conference on Data Mining Workshops (ICDMW 2022). Orlando, FL, USA, 2022-11-30/2022-12-02. DOI.
[374]
K. Riedl.
Leveraging Memory Effects and Gradient Information in Consensus-Based Optimization: On Global Convergence in Mean-Field Law.
Preprint at arXiv (2022). arXiv.
[373]
K. E. Riehm, E. Badillo Goicoechea, F. M. Wang, E. Kim, L. R. Aldridge, C. P. Lupton-Smith, R. Presskreischer, T. H. Chang, S. LaRocca, F. Kreuter and E. A. Stuart.
Association of Non-Pharmaceutical Interventions to Reduce the Spread of SARS-CoV-2 With Anxiety and Depressive Symptoms: A Multi-National Study of 43 Countries.
International Journal of Public Health 67 (2022). DOI.
[372]
P. Roetzer, P. Swoboda, D. Cremers and F. Bernard.
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
GitHub.
[371]
R. Rombach, A. Blattmann, D. Lorenz, P. Esser and B. Ommer.
High-Resolution Image Synthesis with Latent Diffusion Models.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
GitHub.
[370]
R. Rombach, A. Blattmann and B. Ommer.
Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models.
Workshop Vision for Art (VISART VI 2022) at the 17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022-10-23/2022-10-27. arXiv.
[369]
D. Rößle, D. Cremers and T. Schön.
Perceiver Hopfield Pooling for Dynamic Multi-modal and Multi-instance Fusion.
31st International Conference on Artificial Neural Networks (ICANN 2022). Bristol, UK, 2022-09-06/2022-09-09. DOI.
[368]
L. Rottkamp, N. Strauss and M. Schubert.
Efficient On-Street Parking Sensor Placement.
15th International Workshop on Computational Transportation Science (IWCTS 2022) at the 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2022). Seattle, WA, USA, 2022-11-01/2022-11-04. DOI.
[367]
D. Rügamer, A. Bender, S. Wiegrebe, D. Racek, B. Bischl, C. L. Müller and C. Stachl.
Factorized Structured Regression for Large-Scale Varying Coefficient Models.
Preprint at arXiv (2022). arXiv.
[366]
Weiss, S., Hermüller, P. and Westermann, R..
Fast Neural Representations for Direct Volume Rendering.
Computer Graphics Forum 41.6 (2022). DOI.
[365]
H. S. Saadi, V. Hangya, T. Eder and A. Fraser.
Comparative Analysis of Cross-lingual Contextualized Word Embeddings.
2nd Workshop on Multi-lingual Representation Learning (MRL 2022) at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[364]
J. A. Salomon, A. Reinhart, A. Bilinski, E. J. Chua, W. La Motte-Kerr, M. M. Rönn, M. B. Reitsma, K. A. Morris, S. LaRocca, T. H. Farag, F. Kreuter, R. Rosenfeld and R. J. Tibshirani.
The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination.
Proceedings of the National Academy of Sciences 118.51 (2022). DOI.
[363]
L. Sang, B. Haefner, X. Zuo and D. Cremers.
High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF.
Preprint at arXiv (2022). arXiv.
[362]
I. Sarasua, S. Pölsterl and C. Wachinger.
CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis.
25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022-09-18/2022-09-22. DOI.
[361]
A. Saroha, M. Eisenberger, T. Yenamandra and D. Cremers.
Implicit Shape Completion via Adversarial Shape Priors.
Preprint at arXiv (2022). arXiv.
[360]
A. Scagliotti and P. Colli Franzone.
Accelerated subgradient methods.
Preprint at arXiv (2022). arXiv.
[359]
D. Schalk, B. Bischl and D. Rügamer.
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization.
Journal of Computational and Graphical Statistics (2022). DOI.
[358]
D. Schalk, B. Bischl and D. Rügamer.
Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models.
Preprint at arXiv (2022). arXiv.
[357]
D. Schalk, V. S. Hoffmann, B. Bischl and U. Mansmann.
Distributed non-disclosive validation of predictive models by a modified ROC-GLM.
Preprint at arXiv (2022). arXiv.
[356]
E. Schede, J. Brandt, A. Tornede, M. Wever, V. Bengs, E. Hüllermeier and K. Tierney.
A Survey of Methods for Automated Algorithm Configuration.
Journal of Artificial Intelligence Research 75 (2022). DOI.
[355]
M. Schleiss, F. Rouatbi and D. Cremers.
VPAIR--Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments.
Preprint at arXiv (2022). arXiv.
[354]
M. Schneble and G. Kauermann.
Intensity Estimation on Geometric Networks with Penalized Splines.
Annals of Applied Statistics (2022). DOI.
[353]
L. Schneider, F. Pfisterer, P. Kent, J. Branke, B. Bischl and J. Thomas.
Tackling neural architecture search with quality diversity optimization.
1st International Conference on Automated Machine Learning (AutoML-Conf 2022) co-located with the 39th International Conference on Machine Learning (ICML 2022). Baltimore, MD, USA, 2022-07-25/2022-07-27. URL.
[352]
L. Schneider, F. Pfisterer, J. Thomas and B. Bischl.
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models.
Genetic and Evolutionary Computation Conference (GECCO 2022). Boston, MA, USA, 2022-07-09/2022-07-13. DOI.
[351]
L. Schneider, L. Schäpermeier, R. P. Prager, B. Bischl, H. Trautmann and P. Kerschke.
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis.
Preprint at arXiv (2022). arXiv.
[350]
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.
[349]
Y. Scholten, J. Schuchardt, S. Geisler, A. Bojchevski and S. Günnemann.
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[348]
J. Schuchardt and S. Günnemann.
Invariance-Aware Randomized Smoothing Certificates.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[347]
S. Severini, A. Imani, P. Dufter and H. Schütze.
Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages.
13th International Conference on Language Resources and Evaluation (LREC 2022). Marseille, France, 2022-06-21/2022-06-23. URL.
[346]
S. Sharifzadeh, S. M. Baharlou, M. Schmitt, H. Schütze and V. Tresp.
Improving Scene Graph Classification by Exploiting Knowledge from Texts.
36th Conference on Artificial Intelligence (AAAI 2022). Virtual, 2022-02-22/2022-03-01. DOI.
[345]
Y. Shen and D. Cremers.
Deep Combinatorial Aggregation.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. PDF.
[344]
S. Shit, R. Koner, B. Wittmann, J. Paetzold, I. Ezhov, H. Li, J. Pan, S. Sharifzadeh, G. Kaissis, V. Tresp and B. Menze.
Relationformer: A Unified Framework for Image-to-Graph Generation.
17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022-10-23/2022-10-27. DOI.
GitHub.
[343]
H. Silber, F. Gerdon, R. Bach, C. Kern, F. Keusch and F. Kreuter.
A Pre-registered Vignette Experiment on Determinants of Health Data Sharing Behavior: Willingness to Donate Sensor Data, Medical Records, and Biomarkers.
Politics and the Life Sciences (2022). DOI.
[342]
H. Silber, F. Gerdon, R. Bach, C. Kern, F. Keusch and F. Kreuter.
Dataset and Codebook for: 'A Pre-registered Vignette Experiment on Determinants of Health Data Sharing Behavior: Willingness to Donate Sensor Data, Medical Records, and Biomarkers'.
PsychArchives (2022). DOI.
[341]
B. Sischka and G. Kauermann.
EM-Based Smooth Graphon Estimation Using MCMC and Spline-Based Approaches.
Social Networks 68 (2022). DOI.
[340]
M. van Smeden, G. Heinze, B. Van Calster, F. W. Asselbergs, P. E. Vardas, N. Bruining, P. de Jaegere, J. H. Moore, S. Denaxas, A.-L. Boulesteix and K. G. M. Moons.
Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease.
European Heart Journal (2022). DOI.
[339]
C. Sommer, L. Sang, D. Schubert and D. Cremers.
Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[338]
R. Sonabend, A. Bender and S. Vollmer.
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.
Bioinformatics 38.17 (2022). DOI.
[337]
V. Steinborn, P. Dufter, H. Jabbar and H. Schütze.
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models.
Findings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2022). Seattle, WA, USA, 2022-06-10/2022-06-15. DOI.
[336]
N. Strauss, M. Berrendorf, T. Haider and M. Schubert.
A Comparison of Ambulance Redeployment Systems on Real-World Data.
IEEE International Conference on Data Mining Workshops (ICDMW 2022). Orlando, FL, USA, 2022-11-30/2022-12-02. DOI.
GitHub.
[335]
N. Strauß, D. Winkel, M. Berrendorf and M. Schubert.
Reinforcement Learning for Multi-Agent Stochastic Resource Collection.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022-09-19/2022-09-22. DOI.
[334]
D. Strieder and M. Drton.
On the choice of the splitting ratio for the split likelihood ratio test.
Electronic Journal of Statistics 16.2 (2022). DOI.
[333]
L. Stumberg and D. Cremers.
DM-VIO: Delayed Marginalization Visual-Inertial Odometry.
IEEE International Conference on Robotics and Automation (ICRA 2022). Philadelphia, PA, USA, 2022-05-23/2022-05-27. DOI.
[332]
H. Sun, F. G. Conrad and F. Kreuter.
The Carryover Effects of Preceding Interviewer–Respondent Interaction on Responses in Audio Computer-Assisted Self-Interviewing (ACASI).
Journal of Survey Statistics and Methodology 10.2 (2022). DOI.
[331]
A. Toker, L. Kondmann, M. Weber, M. Eisenberger, A. Camero, J. Hu, A. P. Hoderlein, Ç. Şenaras, T. Davis, D. Cremers, G. Marchisio, X. X. Zhu and L. Leal-Taixé.
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24. DOI.
[330]
C. Tomani and D. Cremers.
Challenger: Training with Attribution Maps.
Preprint at arXiv (2022). arXiv.
[329]
C. Tomani, D. Cremers and F. Buettner.
Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration.
17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022-10-23/2022-10-27. DOI.
[328]
M. Trappmann, G.-C. Haas, S. Malich, F. Keusch, S. Bähr, F. Kreuter and S. Schwarz.
Augmenting survey data with digital trace data: Is there a threat to panel retention?.
Journal of Survey Statistics and Methodology (2022). DOI.
[327]
J. Ullerich, M. Windl, A. Bulling and S. Mayer.
ThumbPitch: Enriching Thumb Interaction on Mobile Touchscreens using Deep Learning.
33rd Australian Conference on Human-Computer Interaction (OZCHI 2022). Canberra, NSW, Australia, 2022-11-29/2022-12-02. DOI.
[326]
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.
[325]
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). DOI.
[324]
R. Valliant, J. A. Dever, F. Kreuter and M. R. Valliant.
Package ‘PracTools’.
2022. URL.
[323]
S. Weber, N. Demmel, T. Chon Chan and D. Cremers.
Power Bundle Adjustment for Large-Scale 3D Reconstruction.
Preprint at arXiv (2022). arXiv.
[322]
T. Weber, M. Ingrisch, B. Bischl and D. Rügamer.
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs.
1st Workshop on Medical Applications with Disentanglements (MAD 2022) at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022-09-18/2022-09-22. DOI.
[321]
L. Weissweiler, V. Hofmann, A. Köksal and H. Schütze.
The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Abu Dhabi, United Arab Emirates, 2022-11-07/2022-11-11. URL.
[320]
L. Weissweiler, V. Hofmann, M. Sabet and H. Schütze.
CaMEL: Case Marker Extraction without Labels.
Dublin, Ireland. Dublin, Ireland, 2022-05-22/2022-05-27. DOI.
[319]
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.
Conference on Human Factors in Computing Systems (CHI 2022). New Orleans, LA, USA, 2022-04-30/2022-05-05. DOI.
[318]
M. Windl, N. Henze, A. Schmidt and S. S. Feger.
Automating Contextual Privacy Policies: Design and Evaluation of a Production Tool for Digital Consumer Privacy Awareness.
Conference on Human Factors in Computing Systems (CHI 2022). New Orleans, LA, USA, 2022-04-30/2022-05-05. DOI.
[317]
M. Windl, A. Hiesinger, R. Welsch, A. Schmidt and S. S. Feger.
SaferHome: Interactive Physical and Digital Smart Home Dashboards for Communicating Privacy Assessments to Owners and Bystanders.
ACM Interactive Surfaces and Spaces Conference (ISS 2022). Wellington, New Zealand, 2022-11-20/2022-11-23. DOI.
[316]
M. Windl and S. Mayer.
The Skewed Privacy Concerns of Bystanders in Smart Environments.
ACM International Conference on Mobile Human-Computer Interaction (MobileHCI 2022). Vancouver, Canada, 2022-09-28/2022-10-01. DOI.
[315]
D. Winkel, N. Strauß, M. Schubert and T. Seidl.
Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022-09-19/2022-09-22. DOI.
[314]
M. Wright and B. Ommer.
ArtFID: Quantitative Evaluation of Neural Style Transfer.
German Conference on Pattern Recognition (DAGM-GCPR 2022). Konstanz, Germany, 2022-09-19/2021-09-22. DOI.
GitHub.
[313]
Z. Ye, B. Haefner, Y. Quéau, T. Möllenhoff and D. Cremers.
A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces.
International Journal of Computer Vision (2022). DOI.
[312]
Z. Ye, T. Yenamandra, F. Bernard and D. Cremers.
Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections.
36th Conference on Artificial Intelligence (AAAI 2022). Virtual, 2022-02-22/2022-03-01. DOI.
[311]
Y. Yeganeh, A. Farshad, J. Boschmann, R. Gaus, M. Frantzen and N. Navab.
FedAP: Adaptive Personalization in Federated Learning for Non-IID Data.
3rd Workshop on Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health (DeCaF FAIR 2022) at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022-09-18/2022-09-22. DOI.
[310]
Y. Yeganeh, A. Farshad and N. Navab.
Shape-Aware Masking for Inpainting in Medical Imaging.
Preprint at arXiv (2022). arXiv.
[309]
T. Yenamandra, A. Tewari, N. Yang, F. Bernard, C. Theobalt and D. Cremers.
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions.
Preprint at arXiv (2022). arXiv.
[308]
S. Yuan, A. Maronikolakis and H. Schütze.
Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing.
6th Workshop on Online Abuse and Harms (WOAH 2022). Seattle, WA, USA, 2022-07-14. DOI.
[307]
C. Zelenka, A. Lohrer, M. Bayer and P. Kröger.
AI4EO Hyperview: A SpectralNet3D and RNNPlus Approach for Sustainable Soil Parameter Estimation on Hyperspectral Image Data.
IEEE International Conference on Image Processing (ICIP 2022). Bordeaux, France, 2022-10-16/2022-10-19. DOI.
[306]
M. Zhao, F. Mi, Y. Wang, M. Li, X. Jiang, Q. Liu and H. Schütze.
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework.
Findings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2022). Seattle, WA, USA, 2022-06-10/2022-06-15. DOI.
[305]
S.-F. Zheng, JE. Nam, E. Dorigatti, B. Bischl, S. Azizi and M. Rezaei.
Joint Debiased Representation and Image Clustering Learning with Self-Supervision.
Preprint at arXiv (2022). arXiv.
[304]
Q. Zhou, S. Agostinho, A. Ošep and L. Leal-Taixé.
Is Geometry Enough for Matching in Visual Localization?.
17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022-10-23/2022-10-27. DOI.
[303]
I. Ziegler, B. Ma, E. Nie, B. Bischl, D. Rügamer, B. Schubert and E. Dorigatti.
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?.
Workshop on Learning Meaningful Representations of Life (LMRL 2022) at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022-11-28/2022-12-09. URL.


2021

[302]
M. Ali, M. Berrendorf, M. Galkin, V. Thost, T. Ma, V. Tresp and J. Lehmann.
Improving Inductive Link Prediction Using Hyper-Relational Facts.
20th International Semantic Web Conference (ISWC 2021). Virtual, 2021-10-24/2021-10-28. DOI.
GitHub.
[301]
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.
[300]
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.
[299]
M. Aygun, A. Osep, M. Weber, M. Maximov, C. Stachniss, J. Behley and L. Leal-Taixé.
4D Panoptic LiDAR Segmentation.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
GitHub.
[298]
V. Bauer, D. Harhoff and G. Kauermann.
A smooth dynamic network model for patent collaboration data.
Advances in Statistical Analysis 106 (2021). DOI.
[297]
M. Becker, S. Gruber, J. Richter, J. Moosbauer and B. Bischl.
mlr3hyperband: Hyperband for 'mlr3'.
2021. URL.
GitHub.
[296]
M. Becker, M. Lang, J. Richter, B. Bischl and D. Schalk.
mlr3tuning: Tuning for 'mlr3'.
2021. URL.
GitHub.
[295]
M. Becker, J. Richter, M. Lang, B. Bischl and M. Binder.
bbotk: Black-Box Optimization Toolkit.
2021. URL.
GitHub.
[294]
A. Beer, E. Allerborn, V. Hartmann and T. Seidl.
KISS - A fast kNN-based Importance Score for Subspaces.
24th International Conference on Extending Database Technology (EDBT 2021). Nicosia, Cyprus, 2021-03-23/2021-03-26. PDF.
[293]
A. Beer, L. Stephan and T. Seidl.
LUCKe- Connecting Clustering and Correlation Clustering.
IEEE International Conference on Data Mining Workshops (ICDMW 2021). Auckland, New Zealand, 2021-12-07/2021-12-10. DOI.
[292]
M. Bernhard and M. Schubert.
Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications.
Remote Sensing (2021). URL.
[291]
M. Berrendorf, E. Faerman and V. Tresp.
Active Learning for Entity Alignment.
43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021-03-28/2021-04-01. DOI.
GitHub.
[290]
M. Berrendorf, L. Wacker and E. Faerman.
A Critical Assessment of State-of-the-Art in Entity Alignment.
43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021-03-28/2021-04-01. DOI.
GitHub.
[289]
M. Biloš and S. Günnemann.
Scalable Normalizing Flows for Permutation Invariant Densities.
38th International Conference on Machine Learning (ICML 2021). Virtual, 2021-07-18/2021-07-24. URL.
[288]
M. Binder.
mlrintermbo: Model-Based Optimization for 'mlr3' through 'mlrMBO'.
2021. URL.
GitHub.
[287]
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). URL.
[286]
C. Böhm, M. Perdacher and C. Plant.
A Novel Hilbert Curve for Cache-Locality Preserving Loops.
IEEE Transactions on Big Data 7.2 (2021). DOI.
[285]
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.
[284]
A. Božič, P. Palafox, M. Zollhöfer, J. Thies, A. Dai and M. Nießner.
Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[283]
G. Braso, N. Kister and L. Leal-Taixé.
The Center of Attention: Center-Keypoint Grouping Attention for Multi-Person Pose Estimation.
15th IEEE/CVF International Conference on Computer Vision (ICCV 2021). Virtual, 2021-10-11/2021-10-17. DOI.
[282]
D. Z. Chen, A. Gholami, M. Nießner and A. X. Chang.
Scan2Cap: Context-aware Dense Captioning in RGB-D Scans.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[281]
Z. Chongyu.
GPU-based Data Mining on Android Devices.
.
[280]
S. Coors, D. Schalk, B. Bischl and D. Rügamer.
Automatic Componentwise Boosting: An Interpretable AutoML System.
Automating Data Science Workshop (ADS 2021) at the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD 2021). Virtual, 2021. arXiv.
[279]
M. Dahnert, J. Hou, M. Niessner and A. Dai.
Panoptic 3D Scene Reconstruction From a Single RGB Image.
35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14. PDF.
GitHub.
[278]
A. Dai, Y. Siddiqui, J. Thies, J. Valentin and M. Nießner.
SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[277]
P. Dufter, N. Kassner and H. Schütze.
Static Embeddings as Efficient Knowledge Bases?.
Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021). Virtual, 2021-06-06/2021-06-11. DOI.
[276]
M. Eisenberger, D. Novotny, G. Kerchenbaum, P. Labatut, N. Neverova, D. Cremers and A. Vedaldi.
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
GitHub.
[275]
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). DOI.
[274]
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.
[273]
A. Farshad, S. Musatian, H. Dhamo and N. Navab.
MIGS: Meta Image Generation from Scene Graphs.
32nd British Machine Vision Conference (BMVC 2021). Virtual, 2021-11-22/2021-11-25. URL.
[272]
D. S. Fischer, A. C. Schaar and F. J. Theis.
Learning cell communication from spatial graphs of cells.
Preprint at bioRxiv (2021). DOI.
[271]
T. Frerix, D. Kochkov, J. Smith, D. Cremers, M. Brenner and S. Hoyer.
Variational Data Assimilation with a Learned Inverse Observation Operator.
38th International Conference on Machine Learning (ICML 2021). Virtual, 2021-07-18/2021-07-24. URL.
[270]
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.
[269]
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). DOI.
[268]
M. Fromm, M. Berrendorf, S. Obermeier, T. Seidl and E. Faerman.
Diversity Aware Relevance Learning for Argument Search.
43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021-03-28/2021-04-01. arXiv.
GitHub.
[267]
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.
35th Conference on Artificial Intelligence (AAAI 2021). Virtual, 2021-02-02/2021-02-09. DOI.
GitHub.
[266]
G. Gafni, J. Thies, M. Zollhöfer and M. Nießner.
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[265]
M. Gao, Z. Lähner, J. Thunberg, D. Cremers and F. Bernard.
Isometric Multi-Shape Matching.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
GitHub.
[264]
S. Garg, H. Dhamo, A. Farshad, S. Musatian, N. Navab and F. Tombari.
Unconditional Scene Graph Generation.
15th IEEE/CVF International Conference on Computer Vision (ICCV 2021). Virtual, 2021-10-11/2021-10-17. DOI.
[263]
I. Gerostathopoulos, F. Plášil, C. Prehofer, J. Thomas and B. Bischl.
Automated Online Experiment-Driven Adaptation--Mechanics and Cost Aspects.
IEEE Access 9 (2021). DOI.
[262]
P. Gijsbers, F. Pfisterer, J. van Rijn, B. Bischl and J. Vanschoren.
Meta-Learning for Symbolic Hyperparameter Defaults.
Genetic and Evolutionary Computation Conference (GECCO 2021). Lile, France, 2021-07-10/2021-07-14. DOI.
[261]
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.
[260]
M. Herrmann and F. Scheipl.
A Geometric Perspective on Functional Outlier Detection.
Stats 4.4 (2021). DOI.
[259]
M. Herrmann and F. Scheipl.
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction.
Preprint at arXiv (2021). arXiv.
[258]
S. Hoffmann, F. Schönbrodt, R. Elsas, R. Wilson, U. Strasser and A.-L. Boulesteix.
The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines.
Royal Society Open Science 8.201925 (2021). DOI.
[257]
J. Hou, B. Graham, M. Nießner and S. Xie.
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[256]
A. Imani, J. S. Masoud, L. K. Senel, P. Philipp, F. Yvon and H. Schütze.
Graph Algorithms for Multiparallel Word Alignment.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). Punta Cana, Dominican Republic, 2021-11-07/2021-11-11. DOI.
[255]
A. Imani, M. J. Sabet, P. Dufter, M. Cysouw and H. Schütze.
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus.
Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021). Bangkok, Thailand, 2021-08-01/2021-08-06. DOI.
[254]
N. Kassner, P. Dufter and H. Schütze.
Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models.
16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021). Virtual, 2021-04-19/2021-04-23. DOI.
[253]
N. Kassner, O. Tafjord, H. Schütze and P. Clark.
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). Punta Cana, Dominican Republic, 2021-11-07/2021-11-11. DOI.
[252]
D. Kazempour, A. Beer, M. Oelker, P. Kröger and T. Seidl.
Compound Segmentation via Clustering on Mol2Vec-based Embeddings.
17th IEEE eScience Conference (eScience 2021). Virtual, 2021-09-20/2021-09-23. DOI.
[251]
N. Kees, M. Fromm, E. Faerman and T. Seidl.
Active Learning for Argument Strength Estimation.
2nd Workshop on Insights from Negative Results (Insights 2021) co-located at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). Punta Cana, Dominican Republic, 2021-11-07/2021-11-11. DOI.
[250]
A. Khakzar, S. Baselizadeh, S. Khanduja, C. Rupprecht, S. T. Kim and N. Navab.
Neural Response Interpretation through the Lens of Critical Pathways.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[249]
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.
24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). Strasbourg, France, 2021-09-27/2021-10-01. DOI.
[248]
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.
24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). Strasbourg, France, 2021-09-27/2021-10-01. DOI.
[247]
Q. Khan, P. Wenzel and D. Cremers.
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry.
24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021). Virtual, 2021-04-13/2021-04-15. URL.
[246]
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.
24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). Strasbourg, France, 2021-09-27/2021-10-01. DOI.
[245]
L. Koestler, N. Yang, N. Zeller and D. Cremers.
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo.
Conference on Robot Learning (CoRL 2021). London, UK, 2021-11-08/2021-11-11. PDF.
GitHub.
[244]
G. König, T. Freiesleben and M. Grosse-Wentrup.
A causal perspective on meaningful and robust algorithmic recourse.
Preprint at arXiv (2021). arXiv.
[243]
G. König, C. Molnar, B. Bischl and M. Grosse-Wentrup.
Relative Feature Importance.
25th International Conference on Pattern Recognition (ICPR 2020). Virtual - Milano, Italy, 2021-01-10/2021-01-15. DOI.
[242]
P. Kopper, S. Pölsterl, C. Wachinger, B. Bischl, A. Bender and D. Rügamer.
Semi-Structured Deep Piecewise Exponential Models.
AAAI Spring Symposium Series on Survival Prediction: Algorithms, Challenges and Applications (AAAI-SPACA 2021). Palo Alto, California, USA, 2021-03-21/2021-03-24. PDF.
[241]
M. Lang.
mlr3measures: Performance Measures for 'mlr3'.
2021. URL.
[240]
M. Lang, B. Bischl, J. Richter, X. Sun and M. Binder.
paradox: Define and Work with Parameter Spaces for Complex Algorithms.
2021. URL.
GitHub.
[239]
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.
[238]
C. Leiber, L. G. M. Bauer, B. Schelling, C. Böhm and C. Plant.
Dip-based Deep Embedded Clustering with k-Estimation.
27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). Singapore, 2021-08-14/2021-08-18. DOI.
[237]
B. X. W. Liew, D. Rügamer, A. De Nunzio and D. Falla.
Harnessing time-series kinematic and electromyography signals as predictors to discriminate amongst low back pain recovery status.
Brain and Spine 1 (2021). DOI.
[236]
A. Lohrer, A. Beer, M. Hünemörder, J. Lauterbach, T. Seidl and P. Kröger.
AnyCORE - An Anytime Algorithm for Cluster Outlier REmoval.
Conference on Lernen. Wissen. Daten. Analysen (LWDA 2021). München, Germany, 2021-09-01/2021-09-03. PDF.
[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.
IEEE International Conference on Data Mining Workshops (ICDMW 2021). Auckland, New Zealand, 2021-12-07/2021-12-10. 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 bioRxiv (2021). DOI.
[233]
Y. Ma and V. Tresp.
Causal Inference under Networked Interference and Intervention Policy Enhancement.
24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021). Virtual, 2021-04-13/2021-04-15. URL.
[232]
A. Maronikolakis, P. Dufter and H. Schütze.
BERT Cannot Align Characters.
2nd Workshop on Insights from Negative Results (Insights 2021) co-located at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). Punta Cana, Dominican Republic, 2021-11-07/2021-11-11. DOI.
[231]
A. Maronikolakis, P. Dufter and H. Schütze.
Wine is not v i n. On the Compatibility of Tokenizations across Languages.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). Punta Cana, Dominican Republic, 2021-11-07/2021-11-11. DOI.
[230]
A. Maronikolakis and H. Schütze.
Multidomain Pretrained Language Models for Green NLP.
2nd Workshop on Domain Adaptation for NLP (Adapt-NLP 2021). Kyiv, Ukraine, 2021-04-20. URL.
[229]
A. Maronikolakis, H. Schütze and M. Stevenson.
Identifying Automatically Generated Headlines using Transformers.
4th Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda (NLP4IF 2021). Mexico City, Mexico, 2021-06-06. DOI.
[228]
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.
30th International Joint Conference on Artificial Intelligence ((IJCAI 2021)). Montreal, Canada, 2021-08-19/2021-08-26. DOI.
[227]
M. Mittermeier, M. Weigert and D. Rügamer.
Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach.
Workshop on Tackling Climate Change with Machine Learning at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14. PDF.
[226]
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.
[225]
J. Moosbauer, J. Herbinger, G. Casalicchio, M. Lindauer and B. Bischl.
Explaining Hyperparameter Optimization via Partial Dependence Plots.
35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14. PDF.
GitHub.
[224]
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.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[223]
P. Müller, V. Golkov, V. Tomassini and D. Cremers.
Rotation-Equivariant Deep Learning for Diffusion MRI (short version).
International Society for Magnetic Resonance in Medicine Annual Meeting (ISMRM 2021). Virtual, 2021-05-15/2021-05-20, long version in arXiv. arXiv.
[222]
Y. Nie, J. Hou, X. Han and M. Nießner.
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[221]
S. Obermeier, A. Beer, F. Wahl and T. Seidl.
Cluster Flow — an Advanced Concept for Ensemble-Enabling, Interactive Clustering.
19th Symposium of Database Systems for Business, Technology and Web (BTW 2021). Dresden, Germany, 2021-09-13/2021-09-17. DOI.
[220]
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.
[219]
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.
[218]
F. Pfisterer, J. van Rijn, P. Probst, A. Müller and B. Bischl.
Learning Multiple Defaults for Machine Learning Algorithms.
Genetic and Evolutionary Computation Conference (GECCO 2021). Lile, France, 2021-07-10/2021-07-14. arXiv.
[217]
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.
[216]
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.
[215]
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.
[214]
L. Qian, C. Plant and C. Böhm.
Density-based Clustering for Adaptive Density Variation.
21st IEEE International Conference on Data Mining (ICDM 2021). Auckland, New Zealand, 2021-12-07/2021-12-10. DOI.
[213]
D. Rügamer, F. Pfisterer and P. Baumann.
deepregression: Fitting Semi-Structured Deep Distributional Regression in R.
2021. GitHub.
[212]
S. Schmoll.
Navigation with uncertain spatio-temporal resources.
. DOI.
[211]
S. Schmoll and M. Schubert.
Semi-Markov Reinforcement Learning for Stochastic Resource Collection.
29th International Joint Conference on Artificial Intelligence (IJCAI 2020). Yokohama, Japan (postponed due to the Corona pandemic), 2021-01-07/2021-01-15. DOI.
[210]
C. Scholbeck.
A C++ Library for Extensible Decision Trees.
.
[209]
P. Schratz and M. Becker.
mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'.
2021. URL.
[208]
J. Schuchardt, A. Bojchevski, J. Klicpera and S. Günnemann.
Collective Robustness Certificates - Exploiting Interdependence in Graph Neural Networks.
9th International Conference on Learning Representations (ICLR 2021). Virtual, 2021-05-03/2021-05-07. URL.
[207]
H. Seibold, A. Charlton, A.-L. Boulesteix and S. Hoffmann.
Statisticians roll up your sleeves! There’s a crisis to be solved.
Significance (2021). DOI.
[206]
H. Seibold, S. Czerny, S. Decke, R. Dieterle, T. Eder, S. Fohr, N. Hahn, R. Hartmann, C. Heindl, P. Kopper, D. Lepke, V. Loidl, M. Mandl, S. Musiol, J. Peter, A. Piehler, E. Rojas, S. Schmid, H. Schmidt, M. Schmoll, L. Schneider, X.-Y. To, V. Tran, A. Völker, M. Wagner, J. Wagner, M. Waize, H. Wecker, R. Yang, S. Zellner and M. Nalenz.
A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
PLOS One 16.6 (2021). DOI.
[205]
S. Sharifzadeh, S. M. Baharlou and V. Tresp.
Classification by Attention: Scene Graph Classification with Prior Knowledge.
35th Conference on Artificial Intelligence (AAAI 2021). Virtual, 2021-02-02/2021-02-09. DOI.
[204]
F. Soleymani, M. Eslami, T. Elze, B. Bischl and M. Rezaei.
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images.
Preprint at arXiv (2021). arXiv.
[203]
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.
[202]
N. Strauß, L. Rottkamp, S. Schmoll and M. Schubert.
Efficient Parking Search using Shared Fleet Data.
22nd IEEE International Conference on Mobile Data Management (MDM 2021). Virtual, 2021-06-15/2021-06-18. DOI.
[201]
X. Sun, Y. Yu, Y. Liang, J. Dong, C. Plant and C. Böhm.
Fusing attributed and topological global-relations for network embedding.
Information Sciences 558 (2021). DOI.
[200]
C. Tomani, S. Gruber, M. E. Erdem, D. Cremers and F. Buettner.
Post-hoc Uncertainty Calibration for Domain Drift Scenarios.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25. DOI.
[199]
A. Volkmann, A. Stöcker, F. Scheipl and S. Greven.
Multivariate Functional Additive Mixed Models.
Statistical Modelling (2021). DOI.
[198]
Y. Wang, Y. Shen and D. Cremers.
Explicit pairwise factorized graph neural network for semi-supervised node classification.
Conference on Uncertainty in Artificial Intelligence (UAI 2021). Virtual, 2021-07-27/2021-07-29. PDF.
[197]
M. Weber, J. Xie, M. D. Collins, Y. Zhu, P. Voigtlaender, H. Adam, B. Green, A. Geiger, B. Leibe, D. Cremers, A. Osep, L. Leal-Taixé and L.-C. Chen.
STEP: Segmenting and Tracking Every Pixel.
Track on Datasets and Benchmarks at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14. PDF.
[196]
T. Weber, M. Ingrisch, B. Bischl and D. Rügamer.
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation.
Workshop on Deep Generative Models and Downstream Applications at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14. PDF.
[195]
T. Weber, M. Ingrisch, M. Fabritius, B. Bischl and D. Rügamer.
Survival-oriented embeddings for improving accessibility to complex data structures.
Workshop on Bridging the Gap: from Machine Learning Research to Clinical Practice at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14. arXiv.
[194]
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.
[193]
J. Wrobel, A. Bauer, E. McDonnell and J. Goldsmith.
registr 2.0: Incomplete Curve Registration for Exponential Family Functional Data.
The Journal of Open Source Software 6.61 (2021). DOI.
GitHub.
[192]
Y. Xing, Z. Shi, Z. Meng, G. Lakemeyer, Y. Ma and R. Wattenhofer.
KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation.
Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021). Bangkok, Thailand, 2021-08-01/2021-08-06. DOI.
[191]
W. Ye, D. Mautz, C. Böhm, A. Singh and C. Plant.
Incorporating User's Preference into Attributed Graph Clustering.
IEEE Transactions on Knowledge and Data Engineering 33.12 (2021). DOI.
[190]
Y. Zhang, A. Khakzar, Y. Li, A. Farshad, S. T. Kim and N. Navab.
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information.
35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14. PDF.


2020

[189]
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.
[188]
M. Ali, C. T. Hoyt, L. Vermue, M. Galkin and M. Berrendorf.
pykeen/benchmarking. Version v1.0.
2020. DOI.
[187]
M. C. Altinigneli, L. Miklautz, C. Böhm and C. Plant.
Hierarchical Quick Shift Guided Recurrent Clustering.
36th IEEE International Conference on Data Engineering (ICDE 2020). Dallas, TX, USA, 2020-04-20/2020-04-24. DOI.
[186]
E. Asgari, M. J. Sabet, P. Dufter, C. Ringlstetter and H. Schütze.
Subword Sampling for Low Resource Word Alignment.
Preprint at arXiv (2020). arXiv.
[185]
A. Athar, S. Mahadevan, A. Osep, L. Leal-Taixé and B. Leibe.
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos.
16th European Conference on Computer Vision (ECCV 2020). Virtual, 2020-08-23/2020-08-28. DOI.
[184]
M. Aygün, Z. Lähner and D. Cremers.
Unsupervised Dense Shape Correspondence using Heat Kernels.
8th International Conference on 3D Vision (3DV 2020). Virtual, 2020-11-25/2020-11-28. DOI.
[183]
P. F. M. Baumann, T. Hothorn and D. Rügamer.
Deep Conditional Transformation Models.
Preprint at arXiv (2020). arXiv.
[182]
M. Becker, P. Schratz, M. Lang and B. Bischl.
mlr3fselect: Feature Selection for 'mlr3'.
2020. URL.
[181]
A. Beer, V. Hartmann and T. Seidl.
Orderings of Data - more than a Tripping Hazard.
32nd International Conference on Scientific and Statistical Database Management (SSDBM 2020). Vienna, Austria, 2020-07-07/2020-07-09. DOI.
[180]
A. Beer, D. Kazempour, J. Busch, A. Tekles and T. Seidl.
Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data.
Conference on Lernen. Wissen. Daten. Analysen (LWDA 2020). Bonn, Germany, 2020-09-09/2020-09-11. PDF.
[179]
A. Beer, D. Seeholzer, N. S. Schüler and T. Seidl.
Angle-Based Clustering.
13th International Conference on Similarity Search and Applications (SISAP 2020). Virtual, 2020-09-30/2020-10-02. DOI.
[178]
A. Bender, D. Rügamer, F. Scheipl and B. Bischl.
A General Machine Learning Framework for Survival Analysis.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020). Virtual, 2020-09-14/2020-09-18. DOI.
[177]
M. Berrendorf and E. Faerman.
mberr/ea-active-learning: Zenodo. Version 1.0.1.
2020. DOI.
[176]
M. Berrendorf, E. Faerman, V. Melnychuk, V. Tresp and T. Seidl.
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned.
42nd European Conference on Information Retrieval (ECIR 2020). Virtual, 2020-04-14/2020-04-17. DOI.
GitHub.
[175]
M. Berrendorf, E. Faerman and V. Tresp.
Active Learning for Entity Alignment.
5th International Workshop on Deep Learning for Graphs (DL4G@WWW2020) at the ACM Web Conference 2020 (WWW 2020). Taipeh, Taiwan, 2020-04-21. arXiv.
[174]
M. Berrendorf, E. Faerman, L. Vermue and V. Tresp.
Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank.
IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020). Virtual, 2020-12-14/2020-12-17. DOI.
[173]
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).
5th International Workshop on Deep Learning for Graphs (DL4G@WWW2020) at the ACM Web Conference 2020 (WWW 2020). Taipeh, Taiwan, 2020-04-21, Full papaer at WI-AT 2020. DOI.
[172]
M. Berrendorf, E. Faerman, L. Vermue and V. Tresp.
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods.
Technical Report (2020). arXiv.
[171]
M. Berrendorf, L. Wacker and E. Faerman.
A Critical Assessment of State-of-the-Art in Entity Alignment.
Preprint at arXiv (2020). arXiv.
[170]
M. Berrendorf, L. Wacker and E. Faerman.
mberr/ea-sota-comparison: Zenodo. Version v1.1.1.
2020. DOI.
[169]
A. Beyer, G. Kauermann and H. Schütze.
Embedding Space Correlation as a Measure of Domain Similarity.
12th International Conference on Language Resources and Evaluation (LREC 2020). Marseille, France, 2020-05-13/2020-05-15. URL.
[168]
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.
[167]
M. Binder, J. Moosbauer, J. Thomas and B. Bischl.
Multi-Objective Hyperparameter Tuning and Feature Selection Using Filter Ensembles.
Genetic and Evolutionary Computation Conference (GECCO 2020). Cancun, Mexico, 2020-07-08/2020-07-12. DOI.
[166]
M. Binder, F. Pfisterer and B. Bischl.
Collecting empirical data about hyperparameters for data driven AutoML.
7th Workshop on Automated Machine Learning (AutoML 2020) co-located with ICML 2020. Virtual, 2020-07-18. PDF.
[165]
M. Binder, F. Pfisterer, L. Schneider, B. Bischl, M. Lang and S. Dandl.
mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'.
2020. URL.
GitHub.
[164]
C. Böhm and C. Plant.
Massively Parallel Graph Drawing and Representation Learning.
IEEE International Conference on Big Data (IEEE BigData 2020). Virtual, 2020-12-10/2020-12-13. DOI.
[163]
C. Böhm and C. Plant.
Massively Parallel Random Number Generation.
IEEE International Conference on Big Data (IEEE BigData 2020). Virtual, 2020-12-10/2020-12-13. DOI.
[162]
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). DOI.
[161]
F. Borutta, D. Kazempour, F. Marty, P. Kröger and T. Seidl.
Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform.
24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020). Singapore, 2020-05-11/2020-05-14. DOI.
[160]
A.-L. Boulesteix, A. Charlton, S. Hoffmann and H. Seibold.
A replication crisis in methodological research?.
Significance 17.5 (2020). DOI.
[159]
G. Brasó and L. Leal-Taixé.
Learning a Neural Solver for Multiple Object Tracking.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020-06-14/2020-06-19. DOI.
[158]
J. Busch, E. Faerman, M. Schubert and T. Seidl.
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering.
Workshop on Self-Supervised Learning - Theory and Practice (SSL 2020) at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020-12-06/2020-12-12. arXiv.
GitHub.
[157]
B. Charpentier, D. Zügner and S. Günnemann.
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts.
34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020-12-06/2020-12-12. PDF.
[156]
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.
23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020). Virtual, 2020-10-04/2020-10-08. DOI.
[155]
S. Dandl, C. Molnar, M. Binder and B. Bischl.
Multi-Objective Counterfactual Explanations.
16th International Conference on Parallel Problem Solving from Nature (PPSN 2020). Leiden, Netherlands, 2020-09-05/2020-09-09. DOI.
[154]
D. Davletshina, V. Melnychuk, V. Tran, H. Singla, M. Berrendorf, E. Faerman, M. Fromm and M. Schubert.
Unsupervised Anomaly Detection for X-Ray Images.
Preprint at arXiv (2020). arXiv.
[153]
N. Demmel, M. Gao, E. Laude, T. Wu and D. Cremers.
Distributed Photometric Bundle Adjustment.
8th International Conference on 3D Vision (3DV 2020). Virtual, 2020-11-25/2020-11-28. DOI.
[152]
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.
Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2020) at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020). Virtual, 2020-10-04/2020-10-08. DOI.
[151]
H. Dhamo, A. Farshad, I. Laina, N. Navab, G. D. Hager, F. Tombari and C. Rupprecht.
Semantic Image Manipulation Using Scene Graphs.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020-06-14/2020-06-19. DOI.
[150]
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.
Workshop on Computational Biology (WCB 2020) at the 37th International Conference on Machine Learning (ICML 2020). Virtual, 2020-07-12/2020-07-18. URL.
PDF.
[149]
J. Du, R. Wang and D. Cremers.
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization.
16th European Conference on Computer Vision (ECCV 2020). Virtual, 2020-08-23/2020-08-28. DOI.
[148]
M. Eisenberger, Z. Lähner and D. Cremers.
Smooth Shells: Multi-Scale Shape Registration with Functional Maps.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020-06-14/2020-06-19. DOI.
[147]
M. Eisenberger, A. Toker, L. Leal-Taixé and D. Cremers.
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport.
34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020-12-06/2020-12-12. PDF.
[146]
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.
[145]
G. Fabbro, V. Golkov, T. Kemp and D. Cremers.
Speech Synthesis and Control Using Differentiable DSP.
Preprint at arXiv (2020). arXiv.
[144]
E. Faerman, F. Borutta, J. Busch and M. Schubert.
Ada-LLD: Adaptive Node Similarity Using Multi-Scale Local Label Distributions.
IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020). Virtual, 2020-12-14/2020-12-17. DOI.
[143]
S. Friedl, S. Schmoll, F. Borutta and M. Schubert.
SMART-Env.
21st IEEE International Conference on Mobile Data Management (MDM 2020). Versailles, France, 2020-06-30/2020-07-03. DOI.
[142]
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.
[141]
S. Geisler, D. Zügner and S. Günnemann.
Reliable Graph Neural Networks via Robust Aggregation.
34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020-12-06/2020-12-12. PDF.
[140]
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.
[139]
M. Herrmann.
fda-ndr: Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. R package.
2020. GitHub.
[138]
M. Herrmann.
manifun: Collection of functions to work with embeddings and functional data. R package.
2020. GitHub.
[137]
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.
[136]
M. Herrmann and F. Scheipl.
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction.
Preprint at arXiv (2020). arXiv.
[135]
M. Hildebrandt, J. A. Q. Serna, Y. Ma, M. Ringsquandl, M. Joblin and V. Tresp.
Reasoning on Knowledge Graphs with Debate Dynamics.
34th Conference on Artificial Intelligence (AAAI 2020). New York City, New York, USA, 2020-02-07/2020-02-12. DOI.
[134]
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). DOI.
[133]
B. Holzschuh, Z. Lähner and D. Cremers.
Simulated Annealing for 3D Shape Correspondence.
8th International Conference on 3D Vision (3DV 2020). Virtual, 2020-11-25/2020-11-28. DOI.
[132]
J. Jungmaier, N. Kassner and B. Roth.
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings.
12th International Conference on Language Resources and Evaluation (LREC 2020). Marseille, France, 2020-05-13/2020-05-15. URL.
[131]
N. Kassner, B. Krojer and H. Schütze.
Are Pretrained Language Models Symbolic Reasoners over Knowledge?.
24th Conference on Computational Natural Language Learning (CoNLL 2020). Virtual, 2020-11-19/2020-11-20. DOI.
[130]
N. Kassner and H. Schütze.
BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2020). Virtual, 2020-11-16/2020-11-20. DOI.
[129]
N. Kassner and H. Schütze.
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly.
58th Annual Meeting of the Association for Computational Linguistics (ACL 2020). Virtual, 2020-07-05/2020-07-10. DOI.
[128]
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.
IEEE International Conference on Data Mining Workshops (ICDMW 2020). Sorrento, Italy, 2020-11-17/2020-11-20. DOI.
[127]
D. Kazempour, P. Kröger and T. Seidl.
Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering.
IEEE International Conference on Data Mining Workshops (ICDMW 2020). Sorrento, Italy, 2020-11-17/2020-11-20. DOI.
[126]
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.
IEEE International Conference on Data Mining Workshops (ICDMW 2020). Sorrento, Italy, 2020-11-17/2020-11-20. DOI.
[125]
A. Khakzar, S. Baselizadeh and N. Navab.
Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods.
5th Workshop on Human Interpretability in Machine Learning (WHI 2020) at the 37th International Conference on Machine Learning (ICML 2020). Virtual, 2020-07-12/2020-07-18. arXiv.
[124]
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), *contributed equally to this work. DOI.
[123]
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 50.1 (2020). DOI.
[122]
J. Klicpera, J. Groß and S. Günnemann.
Directional Message Passing for Molecular Graphs.
8th International Conference on Learning Representations (ICLR 2020). Virtual, 2020-04-26/2020-05-01. URL.
[121]
R. Kurle, S. S. Rangapuram, E. de Bézenac, S. Günnemann and J. Gasthaus.
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting.
34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020-12-06/2020-12-12. PDF.
[120]
M. Lang.
mlr3db: Data Base Backend for 'mlr3'.
2020. URL.
GitHub.
[119]
M. Lang.
mlr3oml: Connector Between 'mlr3' and 'OpenML'.
2020. URL.
GitHub.
[118]
M. Lang, Q. Au, S. Coors and P. Schratz.
mlr3learners: Recommended Learners for 'mlr3'.
2020. URL.
GitHub.
[117]
M. Lang, P. Schratz and R. Sonabend.
mlr3viz: Visualizations for 'mlr3'.
2020. URL.
GitHub.
[116]
B. X. W. Liew, A. Peolsson, D. Rugamer, J. Wibault, H. Löfgren, A. Dedering, P. Zsigmond and D. Falla.
Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy – a machine learning approach.
Scientific Reports 10 (2020). DOI.
[115]
B. X. W. Liew, D. Rugamer, A. M De Nunzio and D. Falla.
Interpretable machine learning models for classifying low back pain status using functional physiological variables.
European Spine Journal 29 (2020). DOI.
[114]
B. X. W. Liew, D. Rügamer, A. Stocker and A. M. De Nunzio.
Classifying neck pain status using scalar and functional biomechanical variables -- Development of a method using functional data boosting.
Gait and Posture 76 (2020). DOI.
[113]
M. Lotfollahi, M. Naghipourfar, M. D. Luecken, M. Khajavi, M. Büttner, Ž. Avsec, A. V. Misharin and F. J. Theis.
Query to reference single-cell integration with transfer learning.
Preprint at bioRxiv (2020). DOI.
[112]
Y. Ma, Z. Han and V. Tresp.
Learning with Temporal Knowledge Graphs (Invited Presentation).
CIKM 2020 Workshops (CIKMW 2020) co-located with the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020). Galway, Ireland, 2020-10-19/2020-10-23. PDF.
[111]
Y. Ma and V. Tresp.
A Variational Quantum Circuit Model for Knowledge Graph Embeddings.
1st Workshop on Quantum Tensor Networks in Machine Learning (QTNML 2020) at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020-12-06/2020-12-12. PDF.
[110]
S. Mahadevan, A. Athar, A. Osep, S. Hennen, L. Leal-Taixé and B. Leibe.
Making a Case for 3D Convolutions for Object Segmentation in Videos.
31st British Machine Vision Conference (BMVC 2020). Virtual, 2020-09-07/2020-09-10. URL.
[109]
A. Maldonado, J. Sontheim, F. Richter and T. Seidl.
Performance Skyline: Inferring Process Performance Models from Interval Events.
1st International Workshop on Streaming Analytics for Process Mining (SA4PM 2020) in conjunction with the 2nd International Conference on Process Mining (ICPM 2020). Virtual, 2020-10-04/2020-10-09. DOI.
[108]
A. Markham, A. Chivukula and M. Grosse-Wentrup.
MeDIL: A Python Package for Causal Modelling.
10th International Conference on Probabilistic Graphical Models (PGM 2020). Aalborg, Denmark, 2020-09-23/2020-09-25. URL.
GitHub.
[107]
A. Markham and M. Grosse-Wentrup.
Measurement Dependence Inducing Latent Causal Models.
Conference on Uncertainty in Artificial Intelligence (UAI 2020). Toronto, Canada, 2020-08-04/2020-08-06. URL.
[106]
D. Mautz, C. Plant and C. Böhm.
DeepECT: The Deep Embedded Cluster Tree.
Data Science and Engineering 5 (2020). DOI.
[105]
D. Mautz, W. Ye, C. Plant and C. Böhm.
Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans.
ACM Transactions on Knowledge Discovery from Data 14.5 (2020). DOI.
[104]
L. Miklautz, D. Mautz, C. Altinigneli, C. Böhm and C. Plant.
Deep embedded non-redundant clustering.
34th Conference on Artificial Intelligence (AAAI 2020). New York City, New York, USA, 2020-02-07/2020-02-12. DOI.
[103]
C. Molnar, G. Casalicchio and B. Bischl.
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges.
Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (Workshops ECML-PKDD 2020). Virtual, 2020-09-14/2020-09-18. DOI.
[102]
C. Molnar, G. König, J. Herbinger, T. Freiesleben, S. Dandl, C. A. Scholbeck, G. Casalicchio, M. Grosse-Wentrup and B. Bischl.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.
Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (XXAI 2020) at the 37th International Conference on Machine Learning (ICML 2020). Virtual, 2020-07-12/2020-07-18. DOI.
[101]
S. Obermeier, M. Berrendorf and P. Kröger.
Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation.
IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020). Virtual, 2020-12-14/2020-12-17. DOI.
[100]
M. Perdacher, C. Plant and C. Böhm.
Improved Data Locality Using Morton-order Curve on the Example of LU Decomposition.
IEEE International Conference on Big Data (IEEE BigData 2020). Virtual, 2020-12-10/2020-12-13. DOI.
[99]
C. Plant, S. Biedermann and C. Böhm.
Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning.
26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020). San Diego, California, USA, 2020-08-23/2020-08-27. DOI.
[98]
D. Pulatov and M. Lang.
mlr3cluster: Cluster Extension for 'mlr3'.
2020. URL.
GitHub.
[97]
D. Rügamer, F. Pfisterer and B. Bischl.
Neural Mixture Distributional Regression.
Preprint at arXiv (2020). arXiv.
[96]
M. J. Sabet, P. Dufter, F. Yvon and H. Schütze.
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2020). Virtual, 2020-11-16/2020-11-20. DOI.
[95]
F. Scheipl, J. Goldsmith and J. Wrobel.
tidyfun: Tools for Tidy Functional Data. R package.
2020. URL.
GitHub.
[94]
M. Schneble and G. Kauermann.
Estimation of Latent Network Flows in Bike-Sharing Systems.
Statistical Modelling (2020). DOI.
[93]
P. Schratz, M. Lang, B. Bischl and M. Binder.
mlr3filters: Filter Based Feature Selection for 'mlr3'.
2020. URL.
GitHub.
[92]
T. Seidl.
Keynote: Data Mining on Process Data.
2nd International Conference on Process Mining (ICPM 2020). Virtual, 2020-10-04/2020-10-09. DOI.
[91]
O. Shchur, M. Biloš and S. Günnemann.
Intensity-Free Learning of Temporal Point Processes (selected for spotlight presentation).
8th International Conference on Learning Representations (ICLR 2020). Virtual, 2020. arXiv.
URL.
[90]
O. Shchur, N. Gao, M. Biloš and S. Günnemann.
Fast and Flexible Temporal Point Processes with Triangular Maps.
34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020-12-06/2020-12-12. PDF.
[89]
Y. Shen and D. Cremers.
A Chain Graph Interpretation of Real-World Neural Networks.
Preprint at arXiv (2020). arXiv.
[88]
R. Sonabend, F. Kiraly and M. Lang.
mlr3proba: Probabilistic Supervised Learning for 'mlr3'. R package version 0.2.6.
2020. DOI.
URL.
[87]
R. Sonabend, F. J. Király, A. Bender, B. Bischl and M. Lang.
mlr3proba: Machine Learning Survival Analysis in R.
Preprint at arXiv (2020). arXiv.
[86]
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.
[85]
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). DOI.
[84]
L. von Stumberg, P. Wenzel, N. Yang and D. Cremers.
LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization.
8th International Conference on 3D Vision (3DV 2020). Virtual, 2020-11-25/2020-11-28. DOI.
[83]
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.
42nd German Conference on Pattern Recognition (DAGM-GCPR 2020). Tübingen, Germany, 2020-09-28/2020-10-01. DOI.
[82]
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.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020-06-14/2020-06-19. DOI.
[81]
J. Wrobel, A. Bauer, J. Goldsmith, E. McDonnel and F. Scheipl.
registr: Curve Registration for Exponential Family Functional Data. R package.
2020. GitHub.
[80]
Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixé and X. Alameda-Pineda.
How To Train Your Deep Multi-Object Tracker.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020-06-14/2020-06-19. DOI.
[79]
N. Yang, L. von Stumberg, R. Wang and D. Cremers.
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Virtual, 2020-06-14/2020-06-19. DOI.
[78]
Z. Ye, T. Möllenhoff, T. Wu and D. Cremers.
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning.
23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020). Virtual, 2020-08-26/2020-08-28. URL.
[77]
Y. Yeganeh, A. Farshad, N. Navab and S. Albarqouni.
Inverse Distance Aggregation for Federated Learning with Non-IID Data.
Workshop on Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning (DART DCL 2020) at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020). Virtual, 2020-10-04/2020-10-08. DOI.
[76]
D. Zügner and S. Günnemann.
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbation.
26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020). San Diego, California, USA, 2020-08-23/2020-08-27. DOI.


2019

[75]
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.
[74]
A. Beer, D. Kazempour, M. Baur and T. Seidl.
Human Learning in Data Science (Poster Extended Abstract).
21st International Conference of Human-Computer Interaction (HCII 2019). Orlando, Florida, USA, 2019-07-26/2019-07-31. DOI.
[73]
A. Beer, D. Kazempour and T. Seidl.
Rock - Let the points roam to their clusters themselves.
22nd International Conference on Extending Database Technology (EDBT 2019). Lisbon, Portugal, 2019-03-26/2019-03-29. PDF.
[72]
A. Beer, D. Kazempour, L. Stephan and T. Seidl.
LUCK - Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function (short paper).
31st International Conference on Scientific and Statistical Database Management (SSDBM 2019). Santa Cruz, California, USA, 2019-07-23/2019-07-25. DOI.
[71]
A. Beer, J. Lauterbach and T. Seidl.
MORe++: k-Means Based Outlier Removal on High-Dimensional Data.
12th International Conference on Similarity Search and Applications (SISAP 2019). Newark, New York, USA, 2019. DOI.
[70]
A. Beer, N. S. Schüler and T. Seidl.
A Generator for Subspace Clusters.
Conference on Lernen. Wissen. Daten. Analysen (LWDA 2019). Berlin, Germany, 2019-09-30/2019-10-02. PDF.
[69]
A. Beer and T. Seidl.
Graph Ordering and Clustering - A Circular Approach.
31st International Conference on Scientific and Statistical Database Management (SSDBM 2019). Santa Cruz, California, USA, 2019-07-23/2019-07-25. DOI.
[68]
L. Beggel, M. Pfeiffer and B. Bischl.
Robust Anomaly Detection in Images Using Adversarial Autoencoders.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019-09-16/2019-09-20. DOI.
[67]
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.
[66]
P. Bergmann, T. Meinhardt and L. Leal-Taixé.
Tracking without bells and whistles.
13th IEEE/CVF International Conference on Computer Vision (ICCV 2019). Seoul, Korea, 2019. DOI.
[65]
M. Berrendorf, F. Borutta and P. Kröger.
k-Distance Approximation for Memory-Efficient RkNN Retrieval.
12th International Conference on Similarity Search and Applications (SISAP 2019). Newark, New York, USA, 2019. DOI.
[64]
M. Biloš, B. Charpentier and S. Günnemann.
Uncertainty on Asynchronous Time Event Prediction (Poster).
33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019-12-08/2019-12-14. PDF.
[63]
M. Binder, S. Dandl and J. Moosbauer.
mosmafs: Multi-Objective Simultaneous Model and Feature Selection. R package.
2019. GitHub.
[62]
C. Böhm, M. Perdacher and C. Plant.
A Novel Hilbert Curve for Cache-locality Preserving Loops.
IEEE International Conference on Big Data (IEEE BigData 2019). Los Angeles, CA, USA, 2019-12-09/2019-12-12. DOI.
[61]
A. Bojchevski and S. Günnemann.
Adversarial Attacks on Node Embeddings via Graph Poisoning.
36th International Conference on Machine Learning (ICML 2019). Long Beach, CA, USA, 2019-06-09/2019-06-15. URL.
[60]
A. Bojchevski and S. Günnemann.
Certifiable Robustness to Graph Perturbations.
33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019-12-08/2019-12-14. PDF.
[59]
F. Borutta, J. Busch, E. Faerman, A. Klink and M. Schubert.
Structural Graph Representations based on Multiscale Local Network Topologies.
IEEE/WIC/ACM International Conference on Web Intelligence (WI 2019. Thessaloniki, Greece, 2019-10-14/2019-10-17. DOI.
[58]
F. Borutta, P. Kröger and T. Hubauer.
A Generic Summary Structure for Arbitrarily Oriented Subspace Clustering in Data Streams.
12th International Conference on Similarity Search and Applications (SISAP 2019). Newark, New York, USA, 2019. DOI.
[57]
F. Borutta, S. Schmoll and S. Friedl.
Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning.
27th International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2019). Chicago, ILL, USA, 2019-11-05/2019-11-08. DOI.
[56]
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.
[55]
E. Faerman, M. Rogalla, N. Strauß, A. Krüger, B. Blümel, M. Berrendorf, M. Fromm and M. Schubert.
Spatial Interpolation with Message Passing Framework.
IEEE International Conference on Data Mining Workshops (ICDMW 2019). Beijing, China, 2019-11-08/2019-11-11. DOI.
[54]
E. Faerman, O. Voggenreiter, F. Borutta, T. Emrich, M. Berrendorf and M. Schubert.
Graph Alignment Networks with Node Matching Scores.
Workshop on Graph Representation Learning at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019-12-08/2019-12-14. PDF.
[53]
M. Fromm, M. Berrendorf, E. Faerman, Y. Chen, B. Schüss and M. Schubert.
XD-STOD: Cross-Domain Superresolution for Tiny Object Detection.
IEEE International Conference on Data Mining Workshops (ICDMW 2019). Beijing, China, 2019-11-08/2019-11-11. DOI.
[52]
P. Gijsbers, E. LeDell, Thomas, S. Poirier, B. Bischl and J. Vanschoren.
An Open Source AutoML Benchmark.
6th Workshop on Automated Machine Learning (AutoML 2019) co-located with KDD 2019. Anchorage, AK,USA, 2019-08-05. PDF.
[51]
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.
2019. URL.
[50]
J. Golkov and D. Cremers.
Learning to Evolve.
Preprint at arXiv (2019). arXiv.
[49]
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.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019-09-16/2019-09-20. DOI.
[48]
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.
[47]
J. Held, A. Beer and T. Seidl.
Chain-detection Between Clusters.
Datenbank-Spektrum 19 (2019). DOI.
[46]
J. Held, A. Beer and T. Seidl.
Chain-detection for DBSCAN.
18th Symposium of Database Systems for Business, Technology and Web (BTW 2019). Rostock, Germany, 2019-03-04/2019-03-08. DOI.
[45]
M. Hunemörder, D. Kazempour, A. Beer and T. Seidl.
CODEC - Detecting Linear Correlations in Dense Clusters with Comedian-based PCA.
Conference on Lernen. Wissen. Daten. Analysen (LWDA 2019). Berlin, Germany, 2019-09-30/2019-10-02. PDF.
[44]
D. Kazempour, A. Beer, O. Schrüfer and T. Seidl.
Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents.
Conference on Lernen. Wissen. Daten. Analysen (LWDA 2019). Berlin, Germany, 2019-09-30/2019-10-02. PDF.
[43]
D. Kazempour, A. Beer and T. Seidl.
Data on RAILs: On interactive generation of artificial linear correlated data (Poster Extended Abstract).
21st International Conference of Human-Computer Interaction (HCII 2019). Orlando, Florida, USA, 2019-07-26/2019-07-31. DOI.
[42]
D. Kazempour, K. Emmerig, P. Kröger and T. Seidl.
Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform.
31st International Conference on Scientific and Statistical Database Management (SSDBM 2019). Santa Cruz, California, USA, 2019-07-23/2019-07-25. DOI.
[41]
D. Kazempour, M. Hünemörder and T. Seidl.
On coMADs and Principal Component Analysis.
12th International Conference on Similarity Search and Applications (SISAP 2019). Newark, New York, USA, 2019-10-02/2019-10-04. DOI.
[40]
D. Kazempour, M. Kazakov, P. Kröger and T. Seidl.
DICE: Density-based Interactive Clustering and Exploration.
18th Symposium of Database Systems for Business, Technology and Web (BTW 2019). Rostock, Germany, 2019-03-04/2019-03-08. DOI.
[39]
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.
22nd International Conference on Extending Database Technology (EDBT 2019). Lisbon, Portugal, 2019-03-26/2019-03-29. PDF.
[38]
D. Kazempour and T. Seidl.
Insights into a running clockwork: On interactive process-aware clustering.
22nd International Conference on Extending Database Technology (EDBT 2019). Lisbon, Portugal, 2019-03-26/2019-03-29. PDF.
[37]
D. Kazempour and T. Seidl.
On coMADs and Principal Component Analysis.
12th International Conference on Similarity Search and Applications (SISAP 2019). Newark, New York, USA, 2019-10-02/2019-10-04. DOI.
[36]
D. Kazempour and T. Seidl.
On systematic hyperparameter analysis through the example of subspace clustering.
31st International Conference on Scientific and Statistical Database Management (SSDBM 2019). Santa Cruz, California, USA, 2019-07-23/2019-07-25. DOI.
[35]
D. Kazempour, L. M. Yan and T. Seidl.
From Covariance to Comode in context of Principal Component Analysis.
Conference on Lernen. Wissen. Daten. Analysen (LWDA 2019). Berlin, Germany, 2019-09-30/2019-10-02. PDF.
[34]
S. Kevork and G. Kauermann.
Iterative Estimation of Mixed Exponential Random Graph Models with Nodal Random Effects.
Preprint at arXiv (2019). arXiv.
[33]
J. Klicpera, S. Weißenberger and S. Günnemann.
Diffusion Improves Graph Learning.
33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019-12-08/2019-12-14. PDF.
[32]
G. König and M. Grosse-Wentrup.
A Causal Perspective on Challenges for AI in Precision Medicine.
2nd International Congress on Precision Medicine (PMBC 2019). Munich, Germany, 2019-10-14/2019-10-15.
[31]
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.
[30]
M. Lotfollahi, F. A. Wolf and F. J. Theis.
scGen predicts single-cell perturbation responses.
Nature Methods 16.8 (2019). DOI.
[29]
F. Lüer, D. Mautz and C. Böhm.
Anomaly Detection in Time Series using Generative Adversarial Networks.
IEEE International Conference on Data Mining Workshops (ICDMW 2019). Beijing, China, 2019-11-08/2019-11-11. DOI.
[28]
A. Markham and M. Grosse-Wentrup.
A Causal Semantics for the Edge Clique Cover Problem.
Workshop on Graphical Models: Conditional Independence and Algebraic Structures. TUM, Munich, Germany, 2019. URL.
[27]
A. Markham and M. Grosse-Wentrup.
Measurement Dependence Inducing Latent Causal Models (WS paper).
Workshop on Do the right thing: machine learning and causal inference for improved decision making at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019-12-08/2019-12-14. arXiv.
[26]
D. Mautz, C. Plant and C. Böhm.
Deep Embedded Cluster Tree.
19th IEEE International Conference on Data Mining (ICDM 2019). Beijing, China, 2019-11-08/2019-11-11. DOI.
[25]
M. Moeller, T. Möllenhoff and D. Cremers.
Controlling Neural Networks via Energy Dissipation.
13th IEEE/CVF International Conference on Computer Vision (ICCV 2019). Seoul, Korea, 2019. DOI.
[24]
C. Molnar, G. Casalicchio and B. Bischl.
Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019-09-16/2019-09-20. DOI.
[23]
M. Perdacher, C. Plant and C. Böhm.
Cache-oblivious High-performance Similarity Join.
ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2019). Amsterdam, Netherlands, 2019-06-30/2019-07-05. DOI.
[22]
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.
[21]
F. Pfisterer, S. Coors, J. Thomas and B. Bischl.
Multi-Objective Automatic Machine Learning with AutoxgboostMC.
Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (Workshops ECML-PKDD 2019). Wuerzburg, Germany, 2019-09-16/2019-09-20. arXiv.
[20]
F. Pfisterer, J. Thomas and B. Bischl.
Towards Human Centered AutoML.
Preprint at arXiv (2019). arXiv.
[19]
P. Probst, A.-L. Boulesteix and B. Bischl.
Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
Journal of Machine Learning Research 20 (2019). PDF.
[18]
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). DOI.
[17]
S. Rabanser, S. Günnemann and Z. Lipton.
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift.
33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019-12-08/2019-12-14. PDF.
[16]
S. Schmoll, S. Friedl and M. Schubert.
Scaling the Dynamic Resource Routing Problem.
16th International Symposium on Spatial and Temporal Databases (SSTD 2019). Vienna, Austria, 2019-08-19/2019-08-21. 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.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019-09-16/2019-09-20. DOI.
[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.
Intelligent Systems Conference 2019 ((IntelliSys 2019). London, UK, 2019-09-05/2019-09-06. DOI.
[12]
X. Sun, J. Lin and B. Bischl.
ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, 2019-09-16/2019-09-20. DOI.
[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.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018). Dublin, Ireland, 2018-09-10/2018-09-14. DOI.
[9]
D. Kühn, P. Probst, J. Thomas and B. Bischl.
Automatic Exploration of Machine Learning Experiments on OpenML.
Preprint at arXiv (2018). arXiv.
[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 bioRxiv (2018). DOI.
[7]
I. van Mechelen, A.-L. Boulesteix, R. Dangl, N. Dean, I. Guyon, C. Hennig, F. Leisch and D. Steinley.
Benchmarking in cluster analysis: A white paper.
Preprint at arXiv (2018). arXiv.
[6]
J. Minkwitz, F. Scheipl, E. Binder, C. Sander, U. Hegerl and H. Himmerich.
Generalised functional additive models for brain arousal state dynamics (Poster).
20th International Pharmaco-EEG Society for Preclinical and Clinical Electrophysiological Brain Research Meeting (IPEG 2018). Zurich, Switzerland, 2018-11-21/2018-11-25.
[5]
C. Molnar, G. Casalicchio and B. Bischl.
iml: An R package for interpretable machine learning.
The Journal of Open Source Software 3.26 (2018). DOI.
[4]
J. N. van Rijn, F. Pfisterer, J. Thomas, A. Muller, B. Bischl and J. Vanschoren.
Meta learning for defaults: Symbolic defaults.
Workshop on Meta-Learning (MetaLearn 2018) at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018). Montréal, Canada, 2018-12-03/2018-12-08. PDF.
[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.