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Publications by our Members

2023

[468]
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.
[467]
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.
[466]
R. Koner, T. Hannan, S. Shit, S. Sharifzadeh, M. Schubert, T. Seidl and V. Tresp.
InstanceFormer: An online Video Instance Segmentation Framework.
In Proceedings of the 37th Conference on Artificial Intelligence (AAAI 2023). Washington, DC, USA, 2023. arXiv.
GitHub.
[465]
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.
[464]
D. Rügamer, C. Kolb and N. Klein.
Semi-Structured Distributional Regression.
The American Statistician (2023). DOI.
[463]
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.
[462]
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.
[461]
L. Weissweiler, T. He, N. Otani, D. R. Mortensen, L. Levin and H. Schütze.
Construction Grammar Provides Unique Insight into Neural Language Models.
In Proceedings of the Georgetown University Round Table on Linguistics (GURT 2023). Washington D.C., USA, 2023.


2022

[460]
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.
[459]
D. Alivanistos, M. Berrendorf, M. Cochez and M. Galkin.
Query Embedding on Hyper-Relational Knowledge Graphs.
In Proceedings of the 10th International Conference on Learning Representations (ICLR 2022). Virtual, 2022. PDF.
GitHub.
[458]
R. Bach and F. Kreuter.
Big Data in einer digitalisierten, datengestützten Demokratie.
Demokratie und Öffentlichkeit im 21. Jahrhundert – zur Macht des Digitalen (2022). DOI.
[457]
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.
[456]
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.
[455]
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.
[454]
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.
[453]
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.
[452]
M. Bernhard and M. Schubert.
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations.
In Proceedings of the 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022). Seattle, WA, USA, 2022. GitHub.
[451]
A. Blattmann, R. Rombach, K. Oktay and B. Ommer.
Retrieval-Augmented Diffusion Models.
In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022. arXiv.
[450]
L. Bothmann, K. Peters and B. Bischl.
What Is Fairness? Implications For FairML.
Preprint at arXiv (2022). arXiv.
[449]
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).
[448]
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.
[447]
A. Caelles, T. Meinhardt, G. Brasó and L. Leal-Taixé.
DeVIS: Making Deformable Transformers Work for Video Instance Segmentation.
Preprint at arXiv (2022). arXiv.
[446]
Q. Cheng, N. Zeller and D. Cremers.
Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications.
Preprint at arXiv (2022). arXiv.
[445]
S. Dandl, F. Pfisterer and B. Bischl.
Multi-Objective Counterfactual Fairness.
In Proceedings of the 2022 Genetic and Evolutionary Computation Conference (GECCO 2022). Boston, Massachusetts, USA, 2022. DOI.
[444]
D. Das, Q. Khan and D. Cremers.
Ventriloquist-Net: Leveraging Speech Cues for Emotive Talking Head Generation.
In Proceedings of the IEEE International Conference on Image Processing (ICIP 2022). Bordeaux, France, 2022.
[443]
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.
[442]
D. Deng, F. Karl, F. Hutter, B. Bischl and M. Lindauer.
Efficient Automated Deep Learning for Time Series Forecasting.
In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022. PDF.
[441]
G. De Nicola, B. Sischka and G. Kauermann.
Mixture Models and Networks: The Stochastic Block Model.
Statistical Modelling 22.1-2 (2022). DOI.
[440]
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.
In Proceedings of the 4th Conference on Automated Knowledge Base Construction (AKBC 2022). London, UK, 2022. PDF.
[439]
E. Dorigatti, B. Bischl and B. Schubert.
Improved proteasomal cleavage prediction with positive-unlabeled learning.
Preprint at arXiv (2022). arXiv.
[438]
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.
[437]
E. Dorigatti, J. Schweisthal, B. Bischl and M. Rezaei.
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision.
Preprint at arXiv (2022). arXiv.
[436]
W. Durani, D. Mautz, C. Plant and C. Böhm.
Density-Based Clustering for Highly Varying Density.
In Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL, USA, 2022.
[435]
M. Eisenberger, A. Toker, L. Leal-Taixé, F. Bernard and D. Cremers.
A Unified Framework for Implicit Sinkhorn Differentiation.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022.
[434]
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).
[433]
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.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022.
[432]
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.
In Proceedings of the Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2022) held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022.
[431]
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.
[430]
A. Farshad, A. Makarevich, V. Belagiannis and N. Navab.
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation.
In Proceedings of the 4th Workshop Domain Adaptation and Representation Transfer (DART 2022) held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022.
[429]
A. Farshad, Y. Yeganeh, H. Dhamo, F. Tombari and N. Navab.
DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation.
In Proceedings of the 33rd British Machine Vision Conference (BMVC 2022). London, UK, 2022.
[428]
A. Farshad, Y. Yeganeh, P. Gehlbach and N. Navab.
Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation.
In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 2022.
[427]
B. Felderer, A. Birg and F. Kreuter.
Paradaten.
Handbuch Methoden der empirischen Sozialforschung (2022).
[426]
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.
[425]
V. Fomenko, I. Elezi, D. Ramanan, L. Leal-Taixé and A. Ošep.
Learning to Discover and Detect Objects.
Preprint at arXiv (2022). arXiv.
[424]
C. Frey, Y. Ma and M. Schubert.
SEA: Graph Shell Attention in Graph Neural Networks.
In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022.
[423]
C. Frey and M. Schubert.
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs.
Preprint at arXiv (2022). arXiv.
[422]
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.
[421]
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.
[420]
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.
[419]
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.
[418]
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.
[417]
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.
[416]
G. Fu, Z. Meng, Y. Ma, Z. Han, Z. Ding, M. Schubert and R. Wattenhofer.
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion.
In Proceedings of the 6th ACL Workshop on Structured Prediction for NLP (SPNLP 2022) at the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022). Dublin, Ireland, 2022.
[415]
M. Galkin, M. Berrendorf and C. T. Hoyt.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs.
In Proceedings of the 2022 Workshop on Graph Learning Benchmarks (GLB 2022) at the International World Wide Web Conference (WWW 2022). Virtual, 2022. arXiv.
GitHub.
[414]
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.
[413]
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.
[412]
P. Gijsbers, M. L. P. Bueno, S. Coors, E. LeDell, S. Poirier, J. Thomas, B. Bischl and J. Vanschoren.
AMLB: an AutoML Benchmark.
Preprint at arXiv (2022). arXiv.
[411]
S. Gilhuber, M. Berrendorf, Y. Ma and T. Seidl.
Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations.
In Proceedings of the 6th International Workshop on Interactive Adaptive Learning (IAL2022) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022). Grenoble, France, 2022. GitHub.
[410]
S. Gilhuber, P. Jahn, Y. Ma and T. Seidl.
Verips: Verified Pseudo-label Selection for Deep Active Learning.
In Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL, USA, 2022. GitHub.
[409]
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.
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022). Kyoto, Japan, 2022. arXiv.
[408]
L. Hang, Q. Khan, V. Tresp and D. Cremers.
Biologically Inspired Neural Path Finding.
In Proceedings of the 15th International Conference on Brain Informatics (BI 2022). Padova, Italy, 2022. DOI.
[407]
T. Hannan, R. Koner, J. Kobold and M. Schubert.
Box Supervised Video Segmentation Proposal Network.
Preprint at arXiv (2022). arXiv.
[406]
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.
[405]
J. Herbinger, B. Bischl and G. Casalicchio.
REPID: Regional Effect Plots with implicit Interaction Detection .
In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022). Virtual, 2022. PDF.
[404]
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.
In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022. URL.
[403]
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.
[402]
E. Hohma, C. Frey, A. Beer and T. Seidl.
SCAR - Spectral Clustering Accelerated and Robustified.
In Proceedings of the 48th International Conference on Very Large Databases (VLDB 2022). Sydney, Australia (and hybrid), 2022.
[401]
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.
[400]
C. T. Hoyt, M. Berrendorf, M. Gaklin, V. Tresp and B. M. Gyori.
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs.
In Proceedings of the 2022 Workshop on Graph Learning Benchmarks (GLB 2022) at the International World Wide Web Conference (WWW 2022). Virtual, 2022. arXiv.
[399]
H. H.-H. Hsu, Y. Shen and D. Cremers.
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs.
In Proceedings of the Workshop 'New Frontiers in Graph Learning' at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (NeurIPS 2022 GLFrontiers Workshop). In Proceedings of the Workshop 'New Frontiers in Graph Learning' at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (NeurIPS 2022 GLFrontiers Workshop), 2022.
[398]
H. H.-H. Hsu, Y. Shen, C. Tomani and D. Cremers.
What Makes Graph Neural Networks Miscalibrated?.
In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, 2022. arXiv.
[397]
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.
[396]
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.
[395]
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.
[394]
S. Kevork and G. Kauermann.
Bipartite Exponential Random Graph Models with Nodal Random Effects.
Social Networks 70 (2022). DOI.
[393]
S. Kevork and G. Kauermann.
Iterative Estimation of Mixed Exponential Random Graph Models with Nodal Random Effects.
Network Science 9.4 (2022). DOI.
[392]
A. Khakzar, P. Khorsandi, R. Nobahari and N. Navab.
Do Explanations Explain? Model Knows Best.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022.
[391]
A. Khakzar, Y. Li, 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.
Preprint at arXiv (2022). arXiv.
[390]
A. Kim, G. Brasó, A. Ošep and L. Leal-Taixé.
PolarMOT: How far can geometric relations take us in 3D multi-object tracking?.
In Proceedings of the 17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022.
[389]
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.
[388]
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.
Preprint at arXiv (2022). arXiv.
[387]
S. Klenk, L. Koestler, D. Scaramuzza and D. Cremers.
E-NeRF: Neural Radiance Fields from a Moving Event Camera.
Preprint at arXiv (2022). arXiv.
[386]
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.
[385]
L. Koestler, D. Grittner, M. Moeller, D. Cremers and Z. Lähner.
Intrinsic Neural Fields: Learning Functions on Manifolds.
In Proceedings of the 17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022. arXiv.
[384]
M. Kolmet, Q. Zhou, A. Ošep and L. Leal-Taixé.
Text2Pos: Text-to-Point-Cloud Cross-Modal Localization.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022.
[383]
P. Kopper, S. Wiegrebe, B. Bischl, A. Bender and D. Rügamer.
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis.
In Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022). Chengdu, China, 2022. arXiv.
[382]
J. Lane, B. Kim, F. Kreuter and A. Nunez.
The Value of Science: Special Theme.
Harvard Data Science Review 4.2 (2022). URL.
[381]
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.
[380]
C. Leiber, D. Mautz, C. Plant and C. Böhm.
Automatic Parameter Selection for Non-Redundant Clustering.
In Proceedings of the SIAM International Conference on Data Mining (SDM 2022). Virtual, 2022. DOI.
[379]
T. Liu, Y. Liu, M. Hildebrandt, M. Joblin, H. Li and V. Tresp.
On Calibration of Graph Neural Networks for Node Classification.
In Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN 2022). Padua, Italy, 2022.
[378]
Y. Liu, Y. Ma, M. Hildebrandt, M. Joblin and V. Tresp.
TLogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs.
In Proceedings of the 36th Conference on Artificial Intelligence (AAAI 2022). Virtual, 2022. DOI.
[377]
Y. Liu, S. Yan, L. Leal-Taixé, J. Hays and D. Ramanan.
Soft Augmentation for Image Classification.
Preprint at arXiv (2022). arXiv.
[376]
Y. Liu, I. E. Zulfikar, J. Luiten, A. Dave, D. Ramanan, B. Leibe, A. Ošep and L. Leal-Taixé.
Opening up Open World Tracking.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022.
[375]
Z. Liu, Y. Ma, M. Schubert, Y. Ouyang and Z. Xiong.
Multi-Modal Contrastive Pre-training for Recommendation.
In Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR 2022). Newark, NJ, USA, 2022.
[374]
A. Lohrer, J. J. Binder and P. Kröger.
Group Anomaly Detection for Spatio-Temporal Collective Behaviour Scenarios in Smart Cities.
In Proceedings of the 15th International Workshop on Computational Transportation Science (IWCTS 2022) co-located with the 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022). Seattle, WA, USA, 2022.
[373]
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.
[372]
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.
[371]
A. Maronikolakis, P. Baader and H. Schütze.
Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes.
In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP 2022). Seattle, WA, USA, 2022. DOI.
[370]
A. Maronikolakis, A. Wisiorek, L. Nann, H. Jabbar, S. Udupa and H. Schütze.
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments.
In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022). Dublin, Ireland, 2022. DOI.
[369]
T. Meinhardt, A. Kirillov, L. Leal-Taixé and C. Feichtenhofer.
Trackformer: Multi-object tracking with transformers.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022.
[368]
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.
[367]
J. Moosbauer, G. Casalicchio, M. Lindauer and B. Bischl.
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.
Preprint at arXiv (2022). arXiv.
[366]
D. Muhle, L. Koestler, N. Demmel, F. Bernard and D. Cremers.
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DICE: Density-based Interactive Clustering and Exploration.
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On Co-medians and Principal Component Analysis.
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2018

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G. Casalicchio, C. Molnar and B. Bischl.
Visualizing the feature importance for black box models.
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M. Lotfollahi, F. A. Wolf and F. J. Theis.
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J. Minkwitz, F. Scheipl, E. Binder, C. Sander, U. Hegerl and H. Himmerich.
Generalised functional additive models for brain arousal state dynamics (Poster).
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iml: An R package for interpretable machine learning.
The Journal of Open Source Software 3.26 (2018).
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Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
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Meta learning for defaults: Symbolic defaults.
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Journal of the Royal Statistical Society. Series C (Applied Statistics) 67.3 (2018). DOI.
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D. Schalk, J. Thomas and B. Bischl.
compboost: Modular Framework for Component-wise Boosting.
The Journal of Open Source Software 3.30 (2018).
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Automatic gradient boosting.
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