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Publications by our Members

2024


[847]
J. W. Grootjen, H. Weingärtner and S. Mayer.
Uncovering and Addressing Blink-Related Challenges in Using Eye Tracking for Interactive Systems.
Conference on Human Factors in Computing Systems (CHI 2024). Honolulu, Hawaii, May. 11-16, 2024. To be published.

[846]
L. Haliburton, I. Damen, C. Lallemand, J. Niess, A. Ahtinen and P. W. Woźniak.
Office Wellbeing by Design: Don’t Stand for Anything Less.
Conference on Human Factors in Computing Systems (CHI 2024). Honolulu, Hawaii, May. 11-16, 2024. To be published.

[845]
L. Haliburton, D. J. Grüning, F. Riedel, A. Schmidt and N. Terzimehić.
A Longitudinal In-the-Wild Investigation of Design Frictions to Prevent Smartphone Overuse.
Conference on Human Factors in Computing Systems (CHI 2024). Honolulu, Hawaii, May. 11-16, 2024. To be published. URL.

[844]
S. Sakel, T. Blenk, A. Schmidt and L. Haliburton.
The Social Journal: Investigating Technology to Support and Reflect on Meaningful Social Interactions.
Conference on Human Factors in Computing Systems (CHI 2024). Honolulu, Hawaii, May. 11-16, 2024. To be published. URL.

[843]
V. Bengs, B. Haddenhorst and E. Hüllermeier.
Identifying Copeland Winners in Dueling Bandits with Indifferences.
27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024). Valencia, Spain, May. 02-04, 2024. Accepted.

[842]
D. Dold, D. Rügamer, B. Sick and O. Dürr.
Semi-Structured Subspace Inference.
27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024). Valencia, Spain, May. 02-04, 2024. Accepted.

[841]
N. Palm and T. Nagler.
An Online Bootstrap for Time Series.
27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024). Valencia, Spain, May. 02-04, 2024. Preprint at arXiv. arXiv.

[840]
D. Rügamer.
Scalable Higher-Order Tensor Product Spline Models.
27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024). Valencia, Spain, May. 02-04, 2024. Accepted.

[839]
N. Strauß and M. Schubert.
Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem.
SIAM International Conference on Data Mining (SDM 2024). Houston, TX, USA, Apr. 18-20, 2024. To be published. Preprint at arXiv. arXiv.

[838]
L. Bothmann, S. Dandl and M. Schomaker.
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions.
European Causal Inference Meeting (EUROCIM 2024. Copenhagen, Denmark, Apr. 17-19, 2024. Preprint at arXiv. arXiv.

[837]
E. Artemova, V. Blaschke and B. Plank.
Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. Preprint at arXiv. arXiv.

[836]
J. Baan, R. Fernández, B. Plank and W. Aziz.
Evaluating and Representing Uncertainty in NLP: Two (Conflicting?) Perspectives.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. To be published.

[835]
J. Beck, S. Eckman, B. Ma, R. Chew and F. Kreuter.
Order Effects in Annotation Tasks: Further Evidence of Annotation Sensitivity.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. To be published.

[834]
V. T. Hu, D. Wu, Y. M. Asano, P. Mettes, B. Fernando, B. Ommer and C. G. M. Snoek.
Flow Matching for Conditional Text Generation in a Few Sampling Steps.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. To be published.

[833]
P. Lin, C. Hu, Z. Zhang, A. F. T. Martins and H. Schütze.
mPLM-Sim: Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. Preprint at arXiv. arXiv.

[832]
B. Ma, E. Nie, S. Yuan, H. Schmid, M. Färber, F. Kreuter and H. Schütze.
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. To be published. Preprint at arXiv. arXiv.

[831]
L. K. Şenel, B. Ebing, K. Baghirova, H. Schütze and G. Glavaš.
Kardeş-NLU: Transfer to Low-Resource Languages with Big Brother’s Help – A Benchmark and Evaluation for Turkic Languages.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. To be published. URL.

[830]
M. Zhang, R. van der Goot, M.-Y. Kan and B. Plank.
NNOSE: Nearest Neighbor Occupational Skill Extraction.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. Preprint at arXiv. arXiv.

[829]
M. Zhang, R. van der Goot and B. Plank.
Entity Linking in the Job Market Domain.
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024). St. Julians, Malta, Mar. 17-22, 2024. Preprint at arXiv. arXiv.

[828]
H. Chen, Y. Zhang, D. Krompass, J. Gu and V. Tresp.
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning.
38th Conference on Artificial Intelligence (AAAI 2024). Vancouver, Canada, Feb. 20-27, 2024.

[827]
P. Kolpaczki, V. Bengs, M. Muschalik and E. Hüllermeier.
Approximating the Shapley Value without Marginal Contributions.
36th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI 2024). Vancouver, Canada, Feb. 20-27, 2024. Preprint at arXiv. arXiv.

[826]
T. Ladner and M. Althoff.
Exponent Relaxation of Polynomial Zonotopes and Its Applications in Formal Neural Network Verification.
38th Conference on Artificial Intelligence (AAAI 2024). Vancouver, Canada, Feb. 20-27, 2024.

[825]
J. Lienen and E. Hüllermeier.
Mitigating Label Noise through Data Ambiguation.
38th Conference on Artificial Intelligence (AAAI 2024). Vancouver, Canada, Feb. 20-27, 2024. arXiv.

[824]
M. Muschalik, F. Fumagalli, B. Hammer and E. Hüllermeier.
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.
38th Conference on Artificial Intelligence (AAAI 2024). Vancouver, Canada, Feb. 20-27, 2024. arXiv.

[823]
T. N. Wolf, F. Bongratz, A.-M. Rickmann, S. Pölsterl and C. Wachinger.
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning.
38th Conference on Artificial Intelligence (AAAI 2024). Vancouver, Canada, Feb. 20-27, 2024. arXiv.

[822]
A. Reithmeir, J. A. Schnabel and V. A. Zimmer.
Learning physics-inspired regularization for medical image registration with hypernetworks.
SPIE Medical Imaging: Image Processing 2024. San Diego, CA, USA, Feb. 18-22, 2024. arXiv.
URL.

[821]
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.
Journal of Artificial Intelligence Research 79 (Feb. 17, 2024). DOI.

[820]
R. van Koningsbruggen, L. Haliburton, B. Rossmy, C. George, E. Hornecker and B. Hengeveld.
Metaphors and `Tacit' Data: the Role of Metaphors in Data and Physical Data Representations.
18th International Conference on Tangible, Embedded, and Embodied Interaction. Cork, Ireland, Feb. 11-14, 2024. DOI.

[819]
D. Racek, B. I. Davidson, P. W. Thurner, X. Zhu and G. Kauermann.
The Russian war in Ukraine increased Ukrainian language use on social media.
Communications Psychology 2.1 (Jan. 10, 2024). DOI.

[818]
C. Geldhauser and H. Diebel-Fischer.
Is diverse and inclusive AI trapped in the gap between reality and algorithmizability?.
24th Nordic Conference on Computational Linguistics (NoDaLiDa 2023). Tromsø, Norway, Jan. 09-11, 2024. URL.

[817]
M. Bernhard, R. Amoroso, Y. Kindermann, M. Schubert, L. Baraldi, R. Cucchiara and V. Tresp.
What’s Outside the Intersection? Fine-grained Error Analysis for Semantic Segmentation Beyond IoU.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[816]
A. R. Bhattarai, M. Nießner and A. Sevastopolsky.
TriPlaneNet: An Encoder for EG3D Inversion.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[815]
M. Brahimi, B. Haefner, T. Yenamandra, B. Goldluecke and D. Cremers.
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[814]
M. Z. Darestani, V. Nath, W. Li, Y. He, H. R. Roth, Z. Xu, D. Xu, R. Heckel and C. Zhao.
IR-FRestormer: Iterative Refinement With Fourier-Based Restormer for Accelerated MRI Reconstruction.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[813]
S. Klenk, D. Bonello, L. Koestler, N. Araslanov and D. Cremers.
Masked Event Modeling: Self-Supervised Pretraining for Event Cameras.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[812]
U. Sahin, H. Li, Q. Khan, D. Cremers and V. Tresp.
Enhancing Multimodal Compositional Reasoning of Visual Language Models With Generative Negative Mining.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[811]
T. Weber, M. Ingrisch, B. Bischl and D. Rügamer.
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[810]
T. Yenamandra, A. Tewari, N. Yang, F. Bernard, C. Theobalt and D. Cremers.
FIRe: Fast Inverse Rendering Using Directional and Signed Distance Functions.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[809]
G. Zhang, Y. Zhang, K. Zhang and V. Tresp.
Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoning.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan. 04-08, 2024. URL.

[808]
S. Feuerriegel, J. Hartmann, C. Janiesch and P. Zschech.
Generative AI.
Business and Information Systems Engineering 66.1 (Feb. 2024). DOI.

[807]
H. Boch, A. Fono and G. Kutyniok.
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement.
Preprint at arXiv (Jan. 2024). arXiv.

[806]
C. Cipriani, M. Fornasier and A. Scagliotti.
From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks.
European Journal of Applied Mathematics (2024). To be published. arXiv.

[805]
Z. S. Dunias, B. Van Calster, D. Timmerman, A.-L. Boulesteix and M. van Smeden.
A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study.
Statistics in Medicine (Jan. 2024). DOI.

[804]
K. Hechinger, X. Zhu and G. Kauermann.
Categorising the world into local climate zones: towards quantifying labelling uncertainty for machine learning models.
Journal of the Royal Statistical Society. Series C (Applied Statistics) 73.1 (Jan. 2024). DOI.

[803]
E. Hüllermeier and R. Slowinski.
Preference learning and multiple criteria decision aiding: Differences, commonalities, and synergies -- Part I.
4OR (Jan. 2024). DOI.

[802]
E. Hüllermeier and R. Slowinski.
Preference learning and multiple criteria decision aiding: Differences, commonalities, and synergies -- Part II.
4OR (Jan. 2024). DOI.

[801]
L. Kreitner, J. C. Paetzold, N. Rauch, C. Chen, A. M. Hagag, A. E. Fayed, S. Sivaprasad, S. Rausch, J. Weichsel, B. H. Menze, M. Harders, B. Knier, D. Rückert and M. J. Menten.
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations.
IEEE Transactions on Medical Imaging (Jan. 2024). DOI.

[800]
V. Lehmann, T. Zueger, M. Maritsch, M. Notter, S. Schallmoser, C. Bérubé, C. Albrecht, M. Kraus, S. Feuerriegel, E. Fleisch, T. Kowatsch, S. Lagger, M. Laimer, F. Wortmann and C. Stettler.
Machine Learning to Infer a Health State Using Biomedical Signals --- Detection of Hypoglycemia in People with Diabetes while Driving Real Cars.
NEJM AI (Jan. 2024). DOI.

[799]
M. M. Mandl, A. S. Becker-Pennrich, L. C. Hinske, S. Hoffmann and A.-L. Boulesteix.
Addressing researcher degrees of freedom through minP adjustment.
Preprint at arXiv (Jan. 2024). arXiv.

[798]
T. Papamarkou, M. Skoularidou, L. Aitchison, J. Arbel, D. Dunson, M. Filippone, V. Fortuin, P. Hennig, A. Hubin, A. Immer, T. Karaletsos, M. E. Khan, A. Kristiadi, Y. Li, J. M. H. Lobato, S. Mandt, C. Nemeth, M. A. Osborne, K. Palla, T. G. J. Rudner, D. Rügamer, Y. W. Teh, M. Welling, A. G. Wilson and R. Zhang.
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
Preprint at arXiv. Under Review (2024).

[797]
E. Sommer, L. Wimmer, T. Papamarkou, L. Bothmann, B. Bischl and D. Rügamer.
Connecting the Dots: Is Mode Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?.
Preprint at arXiv. Under review (2024).

[796]
P. Wesp, B. M. Schachtner, K. Jeblick, J. Topalis, M. Weber, F. Fischer, R. Penning, J. Ricke, M. Ingrisch and B. O. Sabel.
Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.
International Journal of Legal Medicine (Jan. 2024). DOI.

[795]
M. Wünsch, C. Sauer, P. Callahan, L. C. Hinske and A.-L. Boulesteix.
From RNA sequencing measurements to the final results: a practical guide to navigating the choices and uncertainties of gene set analysis.
Wiley Interdisciplinary Reviews: Computational Statistics (2024). To be published. arXiv.

[794]
F. Xu, Y. Shi, P. Ebel, W. Yang and X. Zhu.
Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark.
IEEE Transactions on Geoscience and Remote Sensing 62 (Jan. 2024). DOI.

[793]
T. Yang, J. Maly, S. Dirksen and G. Caire.
Plug-In Channel Estimation With Dithered Quantized Signals in Spatially Non-Stationary Massive MIMO Systems.
IEEE Transactions on Communications 72.1 (2024). DOI.

[792]
F. Zhang, Y. Shi, Z. Xiong and X. Zhu.
Few-Shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects.
IEEE Transactions on Geoscience and Remote Sensing 62 (Jan. 2024). DOI.

2023


[791]
H. A. Gündüz, S. Giri, M. Binder, B. Bischl and M. Rezaei.
Uncertainty Quantification of Deep Learning Models for Predicting the Regulatory Activity of DNA Sequences.
22nd IEEE International Conference on Machine Learning and Applications (ICMLA 2023). Jacksonville, Florida, USA, Dec. 15-17, 2023.

[790]
M. Zahn von, O. Hinz and S. Feuerriegel.
Locating disparities in machine learning.
IEEE International Conference on Big Data (IEEE BigData 2023). Sorrento, Italy, Dec. 15-18, 2023. DOI.

[789]
C. Koller, G. Kauermann and X. Zhu.
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance in Earth Observation?.
IEEE Transactions on Geoscience and Remote Sensing 62 (Dec. 12, 2023). DOI.

[788]
S. Chen, J. Gu, Z. Han, Y. Ma, P. Torr and V. Tresp.
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[787]
D. Frauen, V. Melnychuk and S. Feuerriegel.
Sharp Bounds for Generalized Causal Sensitivity Analysis.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[786]
F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier and B. Hammer.
SHAP-IQ: Unified Approximation of any-order Shapley Interactions.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[785]
M. Ghahremani Boozandani and C. Wachinger.
RegBN: Batch Normalization of Multimodal Data with Regularization.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.
PDF.

[784]
T. Klug, D. Atik and R. Heckel.
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[783]
A. Krainovic, M. Soltanolkotabi and R. Heckel.
Learning Provably Robust Estimators for Inverse Problems via Jittering.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[782]
R. Liao, X. Jia, Y. Ma and V. Tresp.
GENTKG: Generative Forecasting on Temporal Knowledge Graph.
Temporal Graph Learning Workshop (TGL 2023) at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[781]
S. Maskey, R. Paolino, A. Bacho and G. Kutyniok.
A Fractional Graph Laplacian Approach to Oversmoothing.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. arXiv.
PDF.

[780]
V. Melnychuk, D. Frauen and S. Feuerriegel.
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[779]
S. Scepanovic, I. Obadic, S. Joglekar, L. GIUSTARINI, C. Nattero, D. Quercia and X. Zhu.
MedSat: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[778]
J. Schweisthal, D. Frauen, V. Melnychuk and S. Feuerriegel.
Reliable Off-Policy Learning for Dosage Combinations.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[777]
M. Singh, A. Fono and G. Kutyniok.
Expressivity of Spiking Neural Networks through the Spike Response Model.
1st Workshop on Unifying Representations in Neural Models (UniReps 2023) at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[776]
G. Zhai, E. P. Örnek, S.-C. Wu, Y. Di, F. Tombari, N. Navab and B. Busam.
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graphs.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[775]
S. Zhang, P. Wicke, L. K. Senel, L. Figueredo, A. Naceri, S. Haddadin, B. Plank and H. Schütze.
LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation.
6th Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec. 10-16, 2023. URL.

[774]
M. Di Marco, K. Hämmerl and A. Fraser.
A Study on Accessing Linguistic Information in Pre-Trained Language Models by Using Prompts.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[773]
E. Garces Arias, V. Pai, M. Schöffel, C. Heumann and M. Aßenmacher.
Automatic transcription of handwritten Old Occitan language.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[772]
M. Giulianelli, J. Baan, W. Aziz, R. Fernández and B. Plank.
What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[771]
V. Hangya, S. Severini, R. Ralev, A. Fraser and H. Schütze.
Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages.
3rd Workshop on Multi-lingual Representation Learning (MRL 2023) at Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[770]
A. H. Kargaran, A. Imani, F. Yvon and H. Schütze.
GlotLID: Language Identification for Low-Resource Languages.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[769]
A. Köksal, T. Schick and H. Schütze.
MEAL: Stable and Active Learning for Few-Shot Prompting.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[768]
A. Köksal, O. Yalcin, A. Akbiyik, M. Kilavuz, A. Korhonen and H. Schütze.
Language-Agnostic Bias Detection in Language Models with Bias Probing.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[767]
W. Lai, A. Chronopoulou and A. Fraser.
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[766]
R. Litschko, M. Müller-Eberstein, R. van der Goot, L. Weber-Genzel and B. Plank.
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[765]
Y. Liu, H. Ye, L. Weissweiler and H. Schütze.
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[764]
M. Müller-Eberstein, R. van der Goot, B. Plank and I. Titov.
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[763]
E. Nie, H. Schmid and H. Schütze.
Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration.
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[762]
X. Wang and B. Plank.
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[761]
L. Weissweiler, V. Hofmann, A. Kantharuban, A. Cai, R. Dutt, A. Hengle, A. Kabra, A. Kulkarni, A. Vijayakumar, H. Yu, H. Schütze, K. Oflazer and D. Mortensen.
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[760]
S. Xu, S. T.y.s.s, O. Ichim, I. Risini, B. Plank and M. Grabmair.
From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification.
Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, Dec. 06-10, 2023. DOI.

[759]
Z. Zhang, H. Yang, B. Ma, D. Rügamer and E. Nie.
Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models.
BabyLM Challenge at 27th Conference on Computational Natural Language Learning (CoNLL 2023). Singapore, Dec. 06-10, 2023. DOI.

[758]
L. Haliburton, B. Rossmy, A. Schmidt and C. George.
An Exploration of Hidden Data: Identifying and Physicalizing Personal Virtual Data to Extend Co-located Communication.
22nd International Conference on Mobile and Ubiquitous Multimedia (MUM 2023). Vienna, Austria, Dec. 03-06, 2023. DOI.

[757]
D. Rügamer, F. Pfisterer, B. Bischl and B. Grün.
Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods.
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k-SubMix: Common Subspace Clustering on Mixed-Type Data.
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ActiveGLAE: A Benchmark for Deep Active Learning with Transformers.
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Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023). Turin, Italy, Sep. 18-22, 2023. Best paper award. DOI.

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IEEE Workshop on Machine Learning for Signal Processing (MLSP 2023). Rome, Italy, Sep. 17-20, 2023. DOI.

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[722]
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OpenML-CTR23 - A curated tabular regression benchmarking suite.
International Conference on Automated Machine Learning (AutoML 2023) - Journal Track. Berlin, Germany, Sep. 12-15, 2023. PDF.

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Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML.
International Conference on Automated Machine Learning (AutoML 2023). Berlin, Germany, Sep. 12-15, 2023. URL.

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A tailored Handwritten-Text-Recognition System for Medieval Latin.
1st Workshop on Ancient Language Processing (ALP 2023) co-located with the Conference on Recent Advances in Natural Language Processing (RANLP 2023). Varna, Bulgaria, Sep. 08, 2023. URL.

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LMU at HaSpeeDe3: Multi-Dataset Training for Cross-Domain Hate Speech Detection.
Final Workshop of the 8th evaluation campaign EVALITA 2023. Parma, Italy, Sep. 07-08, 2023. PDF.

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Automated Side-Channel Attacks using Black-Box Neural Architecture Search.
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DEAR: Dynamic Electric Ambulance Redeployment.
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Connecting the Dots — Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering.
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39th Conference on Uncertainty in Artificial Intelligence (UAI 2023). Pittsburgh, PA, USA, Aug. 01-03, 2023. URL.

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Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?.
39th Conference on Uncertainty in Artificial Intelligence (UAI 2023). Pittsburgh, PA, USA, Aug. 01-03, 2023. URL.

[711]
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Sumformer: Universal Approximation for Efficient Transformers.
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40th International Conference on Machine Learning (ICML 2023). Honolulu, Hawaii, Jul. 23-29, 2023. URL.

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Sparse Modality Regression.
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Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models.
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PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism.
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F. Keusch, S. Bähr, G.-C. Haas, F. Kreuter, M. Trappmann and S. Eckman.
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[361]
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Bipartite Exponential Random Graph Models with Nodal Random Effects.
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Network Science 9.4 (2022). DOI.

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Universal adaptability: Target-independent inference that competes with propensity scoring.
Proceedings of the National Academy of Sciences 119.4 (2022). DOI.

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E-NeRF: Neural Radiance Fields from a Moving Event Camera.
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P. Kocsis, P. Súkenı́k, G. Brasó, M. Nießner, L. Leal-Taixé and I. Elezi.
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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.

[353]
J. Lane, B. Kim, F. Kreuter and A. Nunez.
The Value of Science: Special Theme.
Harvard Data Science Review 4.2 (2022). URL.

[352]
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Soft Augmentation for Image Classification.
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Clinical Ethics – To Compute, or Not to Compute?.
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Sharing Matters for Generalization in Deep Metric Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence 44.1 (2022). DOI.

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[342]
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Efficient and Flexible Sublabel-Accurate Energy Minimization.
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[338]
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Exploring Self-Attention for Crop-type Classification Explainability.
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F. Ott, N. L. Raichur, D. Rügamer, T. Feigl, H. Neumann, B. Bischl and C. Mutschler.
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[336]
F. Ott, D. Rügamer, L. Heublein, T. Hamann, J. Barth, B. Bischl and C. Mutschler.
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International Journal on Document Analysis and Recognition 25.4 (2022). DOI.

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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.
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Alzheimer's and Dementia (2022). DOI.

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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.

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Recurrent events analysis with piece-wise exponential additive mixed models.
Statistical Modelling (2022). DOI.

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Journal of Plasma Physics 88.5 (2022). DOI.

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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.
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Fast Neural Representations for Direct Volume Rendering.
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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.

[327]
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High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF.
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A. Saroha, M. Eisenberger, T. Yenamandra and D. Cremers.
Implicit Shape Completion via Adversarial Shape Priors.
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[325]
A. Scagliotti and P. Colli Franzone.
Accelerated subgradient methods.
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Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models.
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D. Schalk, V. S. Hoffmann, B. Bischl and U. Mansmann.
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ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling.
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[320]
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VPAIR--Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments.
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[319]
M. Schneble and G. Kauermann.
Intensity Estimation on Geometric Networks with Penalized Splines.
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C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl and C. Heumann.
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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.

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PsychArchives (2022). DOI.

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EM-Based Smooth Graphon Estimation Using MCMC and Spline-Based Approaches.
Social Networks 68 (2022). DOI.

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Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease.
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Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.
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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.

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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.

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Challenger: Training with Attribution Maps.
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Augmenting survey data with digital trace data: Is there a threat to panel retention?.
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T. Ullmann, A. Beer, M. Hünemörder, T. Seidl and A.-L. Boulesteix.
Over-optimistic evaluation and reporting of novel cluster algorithms: An illustrative study.
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Validation of cluster analysis results on validation data: A systematic framework.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12.3 (2022). DOI.

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R. Valliant, J. A. Dever, F. Kreuter and M. R. Valliant.
Package ‘PracTools’.
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S. Weber, N. Demmel, T. Chon Chan and D. Cremers.
Power Bundle Adjustment for Large-Scale 3D Reconstruction.
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Z. Ye, B. Haefner, Y. Quéau, T. Möllenhoff and D. Cremers.
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Shape-Aware Masking for Inpainting in Medical Imaging.
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LUCKe- Connecting Clustering and Correlation Clustering.
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OAB - An Open Anomaly Benchmark Framework for Unsupervised and Semisupervised Anomaly Detection on Image and Tabular Data Sets.
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Panoptic 3D Scene Reconstruction From a Single RGB Image.
35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, Dec. 06-14, 2021. PDF.
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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, Dec. 06-14, 2021. PDF.

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J. Moosbauer, J. Herbinger, G. Casalicchio, M. Lindauer and B. Bischl.
Explaining Hyperparameter Optimization via Partial Dependence Plots.
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STEP: Segmenting and Tracking Every Pixel.
Track on Datasets and Benchmarks at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, Dec. 06-14, 2021. PDF.

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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, Dec. 06-14, 2021. PDF.

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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, Dec. 06-14, 2021. arXiv.

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Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information.
35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, Dec. 06-14, 2021. PDF.

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MIGS: Meta Image Generation from Scene Graphs.
32nd British Machine Vision Conference (BMVC 2021). Virtual, Nov. 22-25, 2021. URL.

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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, Nov. 08-11, 2021. PDF.
GitHub.

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A. Imani, M. J. Sabet, L. K. Senel, P. Philipp, F. Yvon and H. Schütze.
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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, Nov. 07-11, 2021. DOI.

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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, Nov. 07-11, 2021. DOI.

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A. Maronikolakis, P. Dufter and H. Schütze.
BERT Cannot Align Characters.
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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, Nov. 07-11, 2021. DOI.

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Improving Inductive Link Prediction Using Hyper-Relational Facts.
20th International Semantic Web Conference (ISWC 2021). Virtual, Oct. 24-28, 2021. DOI.
GitHub.

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G. Braso, N. Kister and L. Leal-Taixé.
The Center of Attention: Center-Keypoint Grouping Attention for Multi-Person Pose Estimation.
IEEE/CVF International Conference on Computer Vision (ICCV 2021). Virtual, Oct. 11-17, 2021. DOI.

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Unconditional Scene Graph Generation.
IEEE/CVF International Conference on Computer Vision (ICCV 2021). Virtual, Oct. 11-17, 2021. DOI.

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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, Sep. 27-Oct. 01, 2021. DOI.

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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, Sep. 27-Oct. 01, 2021. DOI.

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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, Sep. 27-Oct. 01, 2021. DOI.

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Compound Segmentation via Clustering on Mol2Vec-based Embeddings.
17th IEEE eScience Conference (eScience 2021). Virtual, Sep. 20-23, 2021. DOI.

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Automatic Componentwise Boosting: An Interpretable AutoML System.
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Cluster Flow — an Advanced Concept for Ensemble-Enabling, Interactive Clustering.
19th Symposium of Database Systems for Business, Technology and Web (BTW 2021). Dresden, Germany, Sep. 13-17, 2021. DOI.

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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, Sep. 01-03, 2021. PDF.

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Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces.
30th International Joint Conference on Artificial Intelligence ((IJCAI 2021)). Montreal, Canada, Aug. 19-26, 2021. DOI.

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Dip-based Deep Embedded Clustering with k-Estimation.
27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). Singapore, Aug. 14-18, 2021. DOI.

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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, Aug. 01-06, 2021. DOI.

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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, Aug. 01-06, 2021. DOI.

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Explicit pairwise factorized graph neural network for semi-supervised node classification.
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Conference on Lernen. Wissen. Daten. Analysen (LWDA 2019). Berlin, Germany, Sep. 30-Oct. 02, 2019. PDF.

[49]
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, Sep. 16-20, 2019. DOI.

[48]
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, Sep. 16-20, 2019. DOI.

[47]
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, Sep. 16-20, 2019. DOI.

[46]
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, Sep. 16-20, 2019. arXiv.

[45]
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, Sep. 16-20, 2019. DOI.

[44]
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, Sep. 16-20, 2019. DOI.

[43]
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, Sep. 05-06, 2019. DOI.

[42]
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, Aug. 19-21, 2019. DOI.

[41]
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, Aug. 05, 2019. PDF.

[40]
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, Jul. 26-31, 2019. DOI.

[39]
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, Jul. 26-31, 2019. DOI.

[38]
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, CA, USA, Jul. 23-25, 2019. DOI.

[37]
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, CA, USA, Jul. 23-25, 2019. DOI.

[36]
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, CA, USA, Jul. 23-25, 2019. DOI.

[35]
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, CA, USA, Jul. 23-25, 2019. DOI.

[34]
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, Jun. 30-Jul. 05, 2019. DOI.

[33]
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, Jun. 09-15, 2019. URL.

[32]
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, Mar. 26-29, 2019. PDF.

[31]
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, Mar. 26-29, 2019. PDF.

[30]
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, Mar. 26-29, 2019. PDF.

[29]
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, Mar. 04-08, 2019. DOI.

[28]
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, Mar. 04-08, 2019. DOI.

[27]
Q. Au, D. Schalk, G. Casalicchio, R. Schoedel, C. Stachl and B. Bischl.
Component-Wise Boosting of Targets for Multi-Output Prediction.
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[26]
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.

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M. Binder, S. Dandl and J. Moosbauer.
mosmafs: Multi-Objective Simultaneous Model and Feature Selection. R package.
2019. GitHub.

[24]
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.

[23]
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.

[22]
J. Golkov and D. Cremers.
Learning to Evolve.
Preprint at arXiv (2019). arXiv.

[21]
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.

[20]
J. Held, A. Beer and T. Seidl.
Chain-detection Between Clusters.
Datenbank-Spektrum 19 (2019). DOI.

[19]
S. Kevork and G. Kauermann.
Iterative Estimation of Mixed Exponential Random Graph Models with Nodal Random Effects.
Preprint at arXiv (2019). arXiv.

[18]
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.

[17]
M. Lotfollahi, F. A. Wolf and F. J. Theis.
scGen predicts single-cell perturbation responses.
Nature Methods 16.8 (2019). DOI.

[16]
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.

[15]
F. Pfisterer, J. Thomas and B. Bischl.
Towards Human Centered AutoML.
Preprint at arXiv (2019). arXiv.

[14]
P. Probst, A.-L. Boulesteix and B. Bischl.
Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
Journal of Machine Learning Research 20 (2019). PDF.

[13]
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.

[12]
Y. Shen, T. Wu, C. Domokos and D. Cremers.
Probabilistic Discriminative Learning with Layered Graphical Models.
Preprint at arXiv (2019). arXiv.

[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]
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, Dec. 03-08, 2018. PDF.

[9]
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, Nov. 21-25, 2018.

[8]
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, Sep. 10-14, 2018. DOI.

[7]
D. Kühn, P. Probst, J. Thomas and B. Bischl.
Automatic Exploration of Machine Learning Experiments on OpenML.
Preprint at arXiv (2018). arXiv.

[6]
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.

[5]
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.

[4]
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.

[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). DOI.

[1]
J. Thomas, S. Coors and B. Bischl.
Automatic gradient boosting.
Preprint at arXiv (2018). arXiv.