- Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation
Jiajun Wang, Morteza Ghahremani, Yitong Li, Björn Ommer, Christian Wachinger
NeurIPS, 2024 - Non-Parametric Neighborhood Test-Time Generalization: Application to Medical Image Classification
Sameer Ambekar, Julia A. Schnabel, Daniel M. Lang
MICCAI Student Board EMERGE Workshop, 2024 - Probabilistic predictions with Fourier neural operators
Christopher Bülte, Philipp Scholl, Gitta Kutyniok
NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty, 2024 - Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
Hannah Schulz-Kümpel, Sebastian Fischer, Thomas Nagler, Anne-Laure Boulesteix, Bernd Bischl, Roman Hornung
arXiv, 2024 - Look at the Text: Instruction-Tuned Language Models are More Robust Multiple Choice Selectors than You Think
Xinpeng Wang, Chengzhi Hu, Bolei Ma, Paul Röttger, Barbara Plank
1st Conference on Language Modeling (COLM), 2024 - Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations
Sameer Ambekar, Julia A. Schnabel, Cosmin Bereca
1st Conference on Language Modeling (COLM), 2024 - Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning
Amirhossein Vahidi, Lisa Wimmer, Anil Hüseyin Gündüz, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2024 - Probabilistic Self-supervised Learning via Scoring Rules Minimization
Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
International Conference on Learning Representations (ICLR), 2024 - PASTA: Pathology-Aware MRI to PET CroSs-modal TrAnslation with Diffusion Models
Yitong Li, Igor Yakushev, Dennis M. Hedderich, Christian Wachinger
Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024 - Is Personalization Worth It? Notifying Blogs about a Privacy Issue Resulting from Poorly Implemented Consent Banners
Theresa Kriecherbauer, Richard Schwank, Adrian Krauss, Konstantin Neureither, Lian Remme, Melanie Volkamer, Dominik Herrmann
Proceedings of the 19th International Conference on Availability, Reliability and Security, 2024 - Conditional Independence in Stationary Diffusions
Tobias Boege, Mathias Drton, Benjamin Hollering, Sarah Lumpp, Pratik Misra, Daniela Schkoda
arXiv, 2024 - Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
Daniela Schkoda, Elina Robeva, Mathias Drton
arXiv, 2024 - Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images
Zefeng Wang, Zhen Han, Shuo Chen, Fan Xue, Zifeng Ding, Xun Xiao, Volker Tresp, Philip Torr, Jindong Gu
1st Conference of Language Modeling (COLM), 2024 - Data-Driven Optimal Feedback Laws via Kernel Mean Embeddings
Petar Bevanda, Nicolas Hoischen, Stefan Sosnowski, Sandra Hirche, Boris Houska
arXiv, 2024 - Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann
ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2024 - Gaussian Process-Based Representation Learning via Timeseries Symmetries
Petar Bevanda, Max Beier, Armin Lederer, Alexandre Capone, Stefan Georg Sosnowski, Sandra Hirche
ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2024 - Shape completion in the dark: completing vertebrae morphology from 3D ultrasound
Miruna-Alexandra Gafencu, Yordanka Velikova, Mahdi Saleh, Tamas Ungi, Nassir Navab, Thomas Wendler, Mohammad Farid Azampour
International Journal of Computer Assisted Radiology and Surgery, 2024 - Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?
Shuo Chen, Zhen Han, Bailan He, Zifeng Ding, Wenqian Yu, Philip Torr, Volker Tresp, Jindong Gu
ICLR 2024 Workshop on Secure and Trustworthy Large Language Models, 2024 - Lazy Data Practices Harm Fairness Research
Jan Simson, Alessandro Fabris, Christoph Kern
Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024 - Label-wise Aleatoric and Epistemic Uncertainty Quantification
Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier
arXiv, 2024 - Anatomy-aware computed tomography-to-ultrasound spine registration
Mohammad Farid Azampour, Maria Tirindelli, Jane Lameski, Miruna-Alexandra Gafencu, Eleonora Tagliabue, Emad Fatemizadeh, Ilker Hacihaliloglu, Nassir Navab
Medical Physics, 2024 - Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Alicia Curth, Stefan Bauer, Niki Kilbertus, Isaac S. Kohane, Mihaela van der Schaar
Nature Medicine, 2024 - From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Data
Yitong Li, Tom Nuno Wolf, Sebastian Pölsterl, Igor Yakushev, Dennis M. Hedderich, Christian Wachinger
Medical Imaging with Deep Learning, 2024 - VariViT: A Vision Transformer for Variable Image Sizes
Aswathi Varma, Suprosanna Shit, Chinmay Prabhakar, Daniel Scholz, Hongwei Bran Li, bjoern menze, Daniel Rueckert, Benedikt Wiestler
Medical Imaging with Deep Learning, 2024 - Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
Tzu-Yuan Huang, Xiaobing Dai, Sihua Zhang, Alexandre Capone, Velimir Todorovski, Stefan Sosnowski, Sandra Hirche
IEEE Control Systems Letters, 2024 - Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
International Conference on Machine Learning (ICML), 2024 - Explaining Kernel Clustering via Decision Trees
Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
The Twelfth International Conference on Learning Representations, 2024 - Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing
Nicola Franco, Jakob Spiegelberg, Jeanette Miriam Lorenz, Stephan Günnemann
The 40th Conference on Uncertainty in Artificial Intelligence, 2024 - Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
International Conference on Learning Representations (ICLR), 2024 - Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
Konstantin Hess, Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
International Conference on Learning Representations (ICLR), 2024 - A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
International Conference on Learning Representations (ICLR), 2024 - Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Georg Manten, Cecilia Casolo, Emilio Ferrucci, Søren Wengel Mogensen, Cristopher Salvi, Niki Kilbertus
arXiv, 2024 - A Novel Bayes’ Theorem for Upper Probabilities
Michele Caprio, Yusuf Sale, Eyke Hüllermeier, Insup Lee
2024 International Workshop on Epistemic Uncertainty in Artificial Intelligence, 2024 - Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector
Unai Fischer-Abaigar, Christoph Kern, Noam Barda, Frauke Kreuter
Government Information Quarterly, 2024 - More Labels or Cases? Assessing Label Variation in Natural Language Inference
Cornelia Gruber, Katharina Hechinger, Matthias Aßenmacher, Göran Kauermann, Barbara Plank
Proceedings of the Third Workshop on Understanding Implicit and Underspecified Language, 2024
2024
- Automatic Vertebrae Segmentation in MR Volumes
Orgest Xhelili, Miruna Gafencu, Francesca De Benetti, Nassir Navab, Thomas Wendler
Bildverarbeitung für die Medizin 2023, 2023 - Fair Off-Policy Learning from Observational Data
Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
International Conference on Machine Learning (ICML), 2023 - Assessing Robustness via Score-based Adversarial Image Generation
Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann
arXiv, 2023 - Expressivity of graph neural networks through the lens of adversarial robustness
Francesco Campi, Lukas Gosch, Tom Wollschläger, Yan Scholten, Stephan Günnemann
2nd AdvML Frontiers workshop at the 40th International Conference on Machine Learning (ICML), 2023 - mPLM-Sim: Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models
Peiqin Lin, Chengzhi Hu, Zheyu Zhang, André F. T. Martins, Hinrich Schütze
Association for Computational Linguistics (EACL) 2024 Findings, 2023 - Counterfactual Fairness for Predictions using Generative Adversarial Networks
Yuchen Ma, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
arXiv, 2023 - Learning Counterfactually Invariant Predictors
Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus
Transaction on Machine Learning Research, 2023 - LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation
Shengqiang Zhang, Philipp Wicke, Lütfi Kerem Şenel, Luis Figueredo, Abdeldjallil Naceri, Sami Haddadin, Barbara Plank, Hinrich Schütze
UAI workshop causality in time series, 2023 - Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point Clouds
Hector Andrade-Loarca, Julius Hege, Daniel Cremers, Gitta Kutyniok
arXiv, 2023 - Second-Order Uncertainty Quantification: Variance-Based Measures
Yusuf Sale, Paul Hofman, Lisa Wimmer, Eyke Hüllermeier, Thomas Nagler
arXiv, 2023 - Second-Order Uncertainty Quantification: A Distance-Based Approach
Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier
41st International Conference on Machine Learning (ICML 2024), 2023 - Conformal Prediction with Partially Labeled Data
Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier
Twelfth Symposium on Conformal and Probabilistic Prediction with Applications (COPA), 2023 - Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?
Yusuf Sale, Michele Caprio, Eyke Hüllermeier
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), 2023 - A distinct stimulatory cDC1 subpopulation amplifies CD8+ T cell responses in tumors for protective anti-cancer immunity
Philippa Meiser, Moritz A. Knolle, Anna Hirschberger, Gustavo P. de Almeida, Felix Bayerl, Sebastian Lacher, Anna-Marie Pedde, Sophie Flommersfeld, Julian Hönninger, Leonhard Stark, Fabian Stögbauer, Martina Anton, Markus Wirth, Dirk Wohlleber, Katja Steiger, Veit R. Buchholz, Barbara Wollenberg, Christina E. Zielinski, Rickmer Braren, Daniel Rueckert, Percy A. Knolle, Georgios Kaissis, Jan P. Böttcher
Cancer Cell, 2023 - Uncertainty Estimation for Molecules: Desiderata and Methods
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
International Conference on Machine Learning (ICML), 2023 - Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs Using Confidence-Augmented Reinforcement Learning
Zifeng Ding, Jingpei Wu, Zongyue Li, Yunpu Ma, Volker Tresp
Machine Learning and Knowledge Discovery in Databases: Research Track, 2023 - ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
Zifeng Ding, Zongyue Li, Ruoxia Qi, Jingpei Wu, Bailan He, Yunpu Ma, Zhao Meng, Shuo Chen, Ruotong Liao, Zhen Han, Volker Tresp
The Semantic Web (ISWC), 2023 - A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr
arXiv, 2023 - Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
Zifeng Ding, Bailan He, Yunpu Ma, Zhen Han, Volker Tresp
International Joint Conference on Neural Networks (IJCNN), 2023 - Expressivity of Spiking Neural Networks through the Spike Response Model
Manjot Singh, Adalbert Fono, Gitta Kutyniok
UniReps: the First Workshop on Unifying Representations in Neural Models, 2023 - On the Localization of Ultrasound Image Slices within Point Distribution Models
Lennart Bastian, Vincent Bürgin, Ha Young Kim, Alexander Baumann, Benjamin Busam, Mahdi Saleh, Nassir Navab
International Workshop on Shape in Medical Imaging, 2023 - S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences
Lennart Bastian, Alexander Baumann, Emily Hoppe, Vincent Bürgin, Ha Young Kim, Mahdi Saleh, Benjamin Busam, Nassir Navab
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023 - Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks
Vincent Bürgin, Raphael Prevost, Marijn F. Stollenga
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023 - On the Challenges and Practices of Reinforcement Learning from Real Human Feedback
Timo Kaufmann, Sarah Ball, Jacob Beck, Eyke Hüllermeier, Frauke Kreuter
ECML-PKDD HLDM’23 Workshop, 2023 - Seeing ChatGPT Through Students' Eyes: An Analysis of TikTok Data
Anna-Carolina Haensch, Sarah Ball, Markus Herklotz, Frauke Kreuter
Conference: 2023 Big Data Meets Survey Science (BigSurv), 2023 - Non-Parametric Representation Learning with Kernels
Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
AAAI 2024, 2023 - Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
International Joint Conferences on Artificial Intelligence (IJCAI), 2023 - Automated wildlife image classification: An active learning tool for ecological applications
Ludwig Bothmann, Lisa Wimmer, Omid Charrakh, Tobias Weber, Hendrik Edelhoff, Wibke Peters, Hien Nguyen, Caryl Benjamin, Annette Menzel
Ecological Informatics, 2023 - Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, 2023 - Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 - Goodness-of-Fit Tests for Linear Non-Gaussian Structural Equation Models
Daniela Schkoda, Mathias Drton
arXiv, 2023 - Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning
Georgios Kaissis, Alexander Ziller, Stefan Kolek, Anneliese Riess, Daniel Rueckert
NeurIPS, 2023 - Explaining Image Classifiers with Multiscale Directional Image Representation
Stefan Kolek, Robert Windesheim, Hector Andrade Loarca, Gitta Kutyniok, Ron Levie
CVPR, 2023 - occupationMeasurement: A Comprehensive Toolbox for Interactive Occupation Coding in Surveys
Jan Simson, Olga Kononykhina, Malte Schierholz
Journal of Open Source Software, 2023 - How games can make behavioural science better
Bria Long, Jan Simson, Andrés Buxó-Lugo, Duane G. Watson, Samuel A. Mehr
Nature, 2023 - One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions
Jan Simson, Florian Pfisterer, Christoph Kern
The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24), 2023 - Sharp Bounds for Generalized Causal Sensitivity Analysis
Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Thirty-seventh Conference on Neural Information Processing Systems, 2023 - Reliable Off-Policy Learning for Dosage Combinations
Jonas Schweisthal, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Thirty-seventh Conference on Neural Information Processing Systems, 2023 - Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Thirty-seventh Conference on Neural Information Processing Systems, 2023 - Normalizing Flows for Interventional Density Estimation
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
ICML 2023, 2023 - Sources of Uncertainty in Machine Learning -- A Statisticians' View
Cornelia Gruber, Patrick Oliver Schenk, Malte Schierholz, Frauke Kreuter, Göran Kauermann
arXiv, 2023 - Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
Jan Schuchardt, Yan Scholten, Stephan Günnemann
Advances in Neural Information Processing Systems (NeurIPS), 2023 - Hierarchical Randomized Smoothing
Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Advances in Neural Information Processing Systems (NeurIPS), 2023 - Revisiting Robustness in Graph Machine Learning
Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
International Conference on Learning Representations (ICLR), 2023
2023
- Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Zifeng Ding, Jingpei Wu, Bailan He, Yunpu Ma, Zhen Han, Volker Tresp
Conference paper, Automated Knowledge Base Construction, 2022 - Training Differentially Private Graph Neural Networks with Random Walk Sampling
Morgane Ayle, Jan Schuchardt, Lukas Gosch, Daniel Zügner, Stephan Günnemann
Workshop on Trustworthy and Socially Responsible Machine Learning. Conference on Neural Information Processing Systems (NeurIPS), 2022
2022