Publications

    2024


  1. 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
    Nat Med, 2024
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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), 2024
  7. Explaining Kernel Clustering via Decision Trees
    Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
    The Twelfth International Conference on Learning Representations, 2024
  8. 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
  9. Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
    Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
    International Conference on Learning Representations (ICLR), 2024
  10. Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
    Konstantin Hess, Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
    International Conference on Learning Representations (ICLR), 2024
  11. 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
  12. 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
  13. 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

    2023


  1. Fair Off-Policy Learning from Observational Data
    Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
    International Conference on Machine Learning (ICML), 2023
  2. Assessing Robustness via Score-based Adversarial Image Generation
    Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann
    arXiv, 2023
  3. 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
  4. 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
  5. Counterfactual Fairness for Predictions using Generative Adversarial Networks
    Yuchen Ma, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
    arXiv, 2023
  6. Learning Counterfactually Invariant Predictors
    Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus
    arXiv, 2023
  7. 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
    arXiv, 2023
  8. Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point Clouds
    Hector Andrade-Loarca, Julius Hege, Daniel Cremers, Gitta Kutyniok
    arXiv, 2023
  9. Second-Order Uncertainty Quantification: Variance-Based Measures
    Yusuf Sale, Paul Hofman, Lisa Wimmer, Eyke Hüllermeier, Thomas Nagler
    arXiv, 2023
  10. Second-Order Uncertainty Quantification: A Distance-Based Approach
    Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier
    arXiv, 2023
  11. A Novel Bayes’ Theorem for Upper Probabilities
    Michele Caprio, Yusuf Sale, Eyke Hüllermeier, Insup Lee
    arXiv, 2023
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. Bridging the Gap: Towards an Expanded Toolkit for ML-Supported Decision-Making in the Public Sector
    Unai Fischer Abaigar, Christoph Kern, Noam Barda, Frauke Kreuter
    arXiv, 2023
  27. Non-Parametric Representation Learning with Kernels
    Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
    arXiv, 2023
  28. Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
    Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2023
  29. 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
  30. 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
  31. 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
  32. Goodness-of-Fit Tests for Linear Non-Gaussian Structural Equation Models
    Daniela Schkoda, Mathias Drton
    arXiv, 2023
  33. 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
  34. Explaining Image Classifiers with Multiscale Directional Image Representation
    Stefan Kolek, Robert Windesheim, Hector Andrade Loarca, Gitta Kutyniok, Ron Levie
    CVPR, 2023
  35. occupationMeasurement: A Comprehensive Toolbox for Interactive Occupation Coding in Surveys
    Jan Simson, Olga Kononykhina, Malte Schierholz
    Journal of Open Source Software, 2023
  36. How games can make behavioural science better
    Bria Long, Jan Simson, Andrés Buxó-Lugo, Duane G. Watson, Samuel A. Mehr
    Nature, 2023
  37. 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
    arXiv, 2023
  38. Sharp Bounds for Generalized Causal Sensitivity Analysis
    Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
    Thirty-seventh Conference on Neural Information Processing Systems, 2023
  39. Reliable Off-Policy Learning for Dosage Combinations
    Jonas Schweisthal, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
    Thirty-seventh Conference on Neural Information Processing Systems, 2023
  40. 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
  41. Normalizing Flows for Interventional Density Estimation
    Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
    ICML 2023, 2023
  42. Sources of Uncertainty in Machine Learning — A Statisticians’ View
    Cornelia Gruber, Patrick Oliver Schenk, Malte Schierholz, Frauke Kreuter, Göran Kauermann
    arXiv, 2023
  43. 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
  44. Hierarchical Randomized Smoothing
    Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
    Advances in Neural Information Processing Systems (NeurIPS), 2023
  45. Revisiting Robustness in Graph Machine Learning
    Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
    International Conference on Learning Representations (ICLR), 2023

    2022


  1. 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
  2. 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