- Individualized mapping of aberrant cortical thickness via stochastic cortical self-reconstruction
Christian Wachinger, Dennis M. Hedderich, Melissa Thalhammer, Fabian Bongratz
Medical Image Analysis, 2026 - Temporal Neural Cellular Automata: Application to Modeling of Contrast Enhancement in Breast MRI
Daniel M. Lang, Richard Osuala, Veronika Spieker, Karim Lekadir, Rickmer Braren, Julia Schnabel
Lecture Notes in Computer Science, 2026
2026
- Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl, Göran Kauermann, Barbara Plank, Matthias Aßenmacher
4th Workshop on Perspectivist Approaches to NLP, 2025 - Deep Research Brings Deeper Harm
Shuo Chen, Zonggen Li, Zhen Han, Bailan He, Tong Liu, Haokun Chen, Georg Groh, Philip Torr, Volker Tresp, Jindong Gu
The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025 - Conformal Prediction for Causal Effects of Continuous Treatments
Maresa Schröder, Dennis Frauen, Jonas Schweisthal, Konstantin Heß, Valentyn Melnychuk, Stefan Feuerriegel
The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025 - Sequence Modeling with Spectral Mean Flows
Kim Jinwoo, Beier Max, Bevanda Petar, Kim Nayun, Hong Seunghoon
The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025 - ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior
Florian Eichin, Yupei Du, Philipp Mondorf, Maria Matveev, Barbara Plank, Michael A. Hedderich
arXiv, 2025 - The Diashow Paradox: Stronger 3D-Aware Representations Emerge from Image Sets, Not Videos
Duc Nguyen Tien, Sonnweber Anna, Weber Mark, Araslanov Nikita, Cremers Daniel
Structural Priors for Vision Workshop at ICCV'25, 2025 - INR meets Multi-Contrast MRI Reconstruction
Natascha Niessen, Carolin M. Pirkl, Ana Beatriz Solana, Hannah Eichhorn, Veronika Spieker, Wenqi Huang, Tim Sprenger, Marion I. Menzel, Julia A. Schnabel
Reconstruction and Imaging Motion Estimation (RIME) Workshop at MICCAI 2025, 2025 - Beyond Proxy Variables: Extending Refugee Allocation Algorithms for Equitable Predictions
Clara Strasser Ceballos, Marcus Novotny, Christoph Kern
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2025 - Collision Mass Map for Safe and Efficient Human-Robot Interaction
Julian Balletshofer, Robin Jeanne Kirschner, Matthias Althoff
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025), 2025 - SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration
Valentin Margraf, Anna Lappe, Marcel Wever, Carolin Benjamins, Eyke Hüllermeier, Marius Lindauer
NH Malaga Hotel Malaga Spain, 2025 - Quantum Computer Benchmarking: An Explorative Systematic Literature Review
Tobias Rohe, Federico Harjes Ruiloba, Sabrina Egger, Sebastian Beck, Jonas Stein, Claudia Linnhoff-Popien
arXiv, 2025 - Single-cell differential expression analysis between conditions within nested settings
Leon Hafner, Gregor Sturm, Sarah Lumpp, Mathias Drton, Markus List
Briefings in Bioinformatics, 2025 - Learning Interpretable Queries for Explainable Image Classification with Information Pursuit
Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan, Hector Andrade-Loarca, Gitta Kutyniok, Réne Vidal
International Conference on Computer Vision, ICCV 2025, 2025 - VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
Sen Wang, Qing Cheng, Stefano Gasperini, Wei Zhang, Shun-Cheng Wu, Niclas Zeller, Daniel Cremers, Nassir Navab
IEEE Robotics and Automation Letters, 2025 - On weak convergence of Gaussian conditional distributions
Sarah Lumpp, Mathias Drton
Statistics & Probability Letters, 2025 - Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames, Sandra Hirche
The Thirteenth International Conference on Learning Representations (ICLR 2025), 2025 - Location matching on shaky grounds: Re-evaluating algorithms for refugee allocation
Clara Strasser Ceballos, Christoph Kern
FAccT '25: The 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025 - Uncertainty Quantification with Proper Scoring Rules: Adjusting Measures to Prediction Tasks
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
arXiv, 2025 - An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
Christopher Bülte, Yusuf Sale, Timo Löhr, Paul Hofman, Gitta Kutyniok, Eyke Hüllermeier
arXiv, 2025 - Conformal Prediction in Hierarchical Classification
Thomas Mortier, Alireza Javanmardi, Yusuf Sale, Eyke Hüllermeier, Willem Waegeman
arXiv, 2025 - Through the LLM Looking Glass: A Socratic Self-Assessment of Donkeys, Elephants, and Markets
Molly Kennedy, Ayyoob Imani, Timo Spinde, Hinrich Schütze
arXiv, 2025 - Conformal Prediction Regions are Imprecise Highest Density Regions
Michele Caprio, Yusuf Sale, Eyke Hüllermeier
International Symposium on Imprecise Probabilities (ISIPTA), 2025 - Conformal Prediction without Nonconformity Scores
Hanselle Jonas, Javanmardi Alireza, Oberkofler Tobias Florin, Sale Yusuf, Hüllermeier Eyke
The 41st Conference on Uncertainty in Artificial Intelligence, 2025 - Algorithms for reliable decision-making need causal reasoning
Christoph Kern, Unai Fischer-Abaigar, Jonas Schweisthal, Dennis Frauen, Rayid Ghani, Stefan Feuerriegel, Mihaela van der Schaar, Frauke Kreuter
Nature Computational Science, 2025 - PARADIM: A Platform to Support Research at the Interface of Data Science and Medical Imaging
Yannick Lemaréchal, Gabriel Couture, François Pelletier, Ronan Lefol, Pierre-Luc Asselin, Samuel Ouellet, Jérémie Bernard, Leyla Ebrahimpour, Venkata S. K. Manem, Johanna Topalis, Balthasar Schachtner, Sébastien Jodogne, Philippe Joubert, Katharina Jeblick, Michael Ingrisch, Philippe Després
Journal of Imaging Informatics in Medicine, 2025 - The Price of Robustness: Stable Classifiers Need Overparameterization
Berg Jonas von, Fono Adalbert, Datres Massimiliano, Maskey Sohir, Kutyniok Gitta
High-dimensional Learning Dynamics 2025, 2025 - Preventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse
Jan Simson, Fiona Draxler, Samuel Mehr, Christoph Kern
Yokohama Japan, 2025 - Online Selective Conformal Prediction: Errors and Solutions
Yusuf Sale, Aaditya Ramdas
Transactions on Machine Learning Research (TMLR), 2025 - Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
Vit Fojtik, Maria Matveev, Hung-Hsu Chou, Gitta Kutyniok, Johannes Maly
arXiv, 2025 - Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
Christopher Bülte, Sohir Maskey, Philipp Scholl, Jonas Berg, Gitta Kutyniok
ICLR 2025 Workshop on Tackling Climate Change with Machine Learning, 2025 - Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
ICLR 2025 Workshop on Advances in Financial AI: Opportunities, Innovations and Responsible AI, 2025 - Cracking the Code: Evaluating Zero-Shot Prompting Methods for Providing Programming Feedback
Ippisch Niklas, Haensch Anna-Carolina, Herklotz Markus, Simson Jan, Beck Jacob, Schierholz Malte
ICLR 2025 Workshop on Human-AI Coevolution, 2025 - Differentially private learners for heterogeneous treatment effects
Maresa Schröder, Valentyn Melnychuk, Stefan Feuerriegel
The Thirteenth International Conference on Learning Representations (ICLR), 2025 - Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation
Xinpeng Wang, Chengzhi Hu, Paul Röttger, Barbara Plank
The Thirteenth International Conference on Learning Representations (ICLR), 2025 - Probabilistic neural operators for functional uncertainty quantification
Christopher Bülte, Philipp Scholl, Gitta Kutyniok
Transactions on Machine Learning Research, 2025 - Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions, International Conference on Learning Representations (ICLR), 2025 - ParFam -- (Neural Guided) Symbolic Regression via Continuous Global Optimization
Philipp Scholl, Katharina Bieker, Hillary Hauger, Gitta Kutyniok
The Thirteenth International Conference on Learning Representations (ICLR), 2025 - The Value of Prediction in Identifying the Worst-Off
Unai Fischer-Abaigar, Christoph Kern, Juan Carlos Perdomo
International Conference on Learning Representations (ICLR), 2025 - Exact Certification of (Graph) Neural Networks Against Label Poisoning
Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar
International Conference on Learning Representations (ICLR), 2025 - A Probabilistic Perspective on Unlearning and Alignment for Large Language Models
Yan Scholten, Stephan Günnemann, Leo Schwinn
International Conference on Learning Representations (ICLR), 2025 - Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning
Yan Scholten, Stephan Günnemann
International Conference on Learning Representations (ICLR), 2025 - Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?
Shuo Chen, Zhen Han, Bailan He, Jianzhe Liu, Mark Buckley, Yao Qin, Philip Torr, Volker Tresp, Jindong Gu
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025 - Robust Score Matching
Richard Schwank, Andrew McCormack, Mathias Drton
arXiv, 2025 - Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2025 - Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann
The Thirteenth International Conference on Learning Representations (ICLR 2025), and ICML 2024 Gram workshop (oral and best paper runner-up award), 2025 - Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Georg Manten, Cecilia Casolo, Emilio Ferrucci, Søren Wengel Mogensen, Cristopher Salvi, Niki Kilbertus
The Thirteenth International Conference on Learning Representations (ICLR), 2025 - Goodness-of-Fit Tests for Linear Non-Gaussian Structural Equation Models
Daniela Schkoda, Mathias Drton
Biometrika, 2025 - Generalization Bounds for Message Passing Networks on Mixture of Graphons
Sohir Maskey, Gitta Kutyniok, Ron Levie
SIAM Journal on Mathematics of Data Science, 2025 - An Asymmetric Independence Model for Causal Discovery on Path Spaces
Georg Manten, Cecilia Casolo, Søren Wengel. Mogensen, Niki Kilbertus
Proceedings of Machine Learning Research, 2025 - Mathematical algorithm design for deep learning under societal and judicial constraints: The algorithmic transparency requirement
Holger Boche, Adalbert Fono, Gitta Kutyniok
Applied and Computational Harmonic Analysis, 2025 - Your Assumed DAG is Wrong And Here’s How To Deal With It
Kirtan Padh, Zhufeng Li, Cecilia Casolo, Niki Kilbertus
Proceedings of Machine Learning Research, 2025 - Complex-Valued Federated Learning with Differential Privacy and MRI Applications
Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia Schnabel, Georgios Kaissis
Lecture Notes in Computer Science, 2025 - Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Kristian Schwethelm, Johannes Kaiser, Jonas Kuntzer, Mehmet Yiǧitsoy, Daniel Rückert, Georgios Kaissis
IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2025 - Motion-robust T*2 quantification from low-resolution gradient echo brain MRI with physics-informed deep learning
Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Lina Felsner, Kilian Weiss, Christine Preibisch, Julia Schnabel
Magnetic Resonance in Medicine, 2025 - Cross-Domain and Cross-Dimension Learning for Image-to-Graph Transformers
Alexander H. Berger, Laurin Lux, Suprosanna Shit, Ivan Ezhov, Georgios Kaissis, Martin J. Menten, Daniel Rueckert, Johannes C. Paetzold
Proceedings 2025 IEEE Winter Conference on Applications of Computer Vision Wacv, 2025 - Visual Privacy Auditing with Diffusion Models
Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Sarah Lockfisch, Daniel Rückert, Alexander Ziller
Transactions on Machine Learning Research, 2025 - Koopman-Equivariant Gaussian Processes
Petar Bevanda, Max Beier, Alexandre Capone, Stefan Sosnowski, Sandra Hirche, Armin Lederer
Proceedings of Machine Learning Research, 2025 - Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility Through Quantization
Holger Boche, Vit Fojtik, Adalbert Fono, Gitta Kutyniok
Journal of Fourier Analysis and Applications, 2025 - Task-Oriented Visual Object Pose Estimation for Robot Manipulation: A Modular Approach
Ahmed Abdelrahman, Peter So, Hoan Quang Le, Abdalla Swikir, Sami Haddadin
Springer Proceedings in Advanced Robotics, 2025 - Aleatoric and Epistemic Uncertainty in Conformal Prediction
Yusuf Sale, Alireza Javanmardi, Eyke Hüllermeier
Proceedings of Machine Learning Research, 2025 - On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
Deniz Daum, Richard Osuala, Anneliese Riess, Georgios Kaissis, Julia A. Schnabel, Maxime Di Folco
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2025 - Enhancing the Utility of Privacy-Preserving Cancer Classification Using Synthetic Data
Richard Osuala, Daniel M. Lang, Anneliese. Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia Schnabel, Karim Lekadir
Lecture Notes in Computer Science, 2025 - Inverse problems are solvable on real number signal processing hardware
Holger Boche, Adalbert Fono, Gitta Kutyniok
Applied and Computational Harmonic Analysis, 2025 - Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives
Ayhan Can Erdur, Daniel Rusche, Daniel Scholz, Johannes Kiechle, Stefan Fischer, Óscar Llorián-Salvador, Josef A. Buchner, Mai Q. Nguyen, Lucas Etzel, Jonas Weidner, Marie-Christin Metz, Benedikt Wiestler, Julia Schnabel, Daniel Rueckert, Stephanie E. Combs, Jan C. Peeken
Strahlentherapie und Onkologie, 2025 - Infinite Width Limits of Self Supervised Neural Networks
Maximilian Fleissner, Gautham Govind Anil, Debarghya Ghoshdastidar
Proceedings of Machine Learning Research, 2025
2025
- Problem Solving Through Human-AI Preference-Based Cooperation
Subhabrata Dutta, Timo Kaufmann, Goran Glavaš, Ivan Habernal, Kristian Kersting, Frauke Kreuter, Mira Mezini, Iryna Gurevych, Eyke Hüllermeier, Hinrich Schuetze
arXiv, 2024 - Toward Near-Globally Optimal Nonlinear Model Predictive Control via Diffusion Models
Tzu-Yuan Huang, Armin Lederer, Nicolas Hoischen, Jan Brüdigam, Xuehua Xiao, Stefan Sosnowski, Sandra Hirche
7th Annual Learning for Dynamics & Control Conference (L4DC 2025), 2024 - Kernel-Based Optimal Control: An Infinitesimal Generator Approach
Petar Bevanda, Nicolas Hoischen, Tobias Wittmann, Jan Brüdigam, Sandra Hirche, Boris Houska
7th Annual Learning for Dynamics & Control Conference (L4DC 2025), 2024 - Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann
Conference on Neural Information Processing Systems (NeurIPS) 2024 AdvML-Frontiers Workshop, and Transactions on Machine Learning Research, 2024 - Unifying Local and Global Shape Descriptors to Grade Soft-Tissue Sarcomas Using Graph Convolutional Networks
Johannes Kiechle, Stefan M. Fischer, Daniel M. Lang, Maxime Di Folco, Sarah C. Foreman, Verena K. N. Rösner, Ann-Kathrin Lohse, Carolin Mogler, Carolin Knebel, Marcus R. Makowski, Klaus Woertler, Stephanie E. Combs, Alexandra S. Gersing, Jan C. Peeken, Julia A. Schnabel
2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024 - Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?
Johannes Kiechle, Daniel M. Lang, Stefan M. Fischer, Lina Felsner, Jan C. Peeken, Julia A. Schnabel
MICCAI 2024 - GRAIL Workshop, 2024 - Investigating the role of morphology in deep learning-based liposarcoma grading
Johannes Kiechle, Sarah C. Foreman, Stefan Fischer, Daniel Rusche, Verena Rösner, Ann-Kathrin Lohse, Carolin Mogler, Stephanie E. Combs, Marcus R. Makowski, Klaus Woertler, Daniel M. Lang, Julia A. Schnabel, Alexandra S. Gersing, Jan C. Peeken
Radiotherapy and Oncology, 2024 - Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
Sarah Ball, Frauke Kreuter, Nina Panickssery
arXiv, 2024 - DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers Using MRI and PET
Yitong Li, Morteza Ghahremani, Youssef Wally, Christian Wachinger
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025, 2024 - Quantifying Aleatoric and Epistemic Uncertainty: A Credal Approach
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
International Conference on Machine Learning (ICML) 2024 Workshop on Structured Probabilistic Inference and Generative Modeling, 2024 - Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
arXiv, 2024 - Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets
Wang Yuxin, Schröder Maresa, Frauen Dennis, Schweisthal Jonas, Hess Konstantin, Feuerriegel Stefan
The Thirteenth International Conference on Learning Representations (ICLR), 2024 - Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models
Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton
International Conference on Probabilistic Graphical Models, 2024 - Cross-validating causal discovery via Leave-One-Variable-Out
Daniela Schkoda, Philipp Faller, Patrick Blöbaum, Dominik Janzing
arXiv, 2024 - The Missing Link: Allocation Performance in Causal Machine Learning
Unai Fischer-Abaigar, Christoph Kern, Frauke Kreuter
arXiv, 2024 - Relaxing Graph Transformers for Adversarial Attacks
Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann
arXiv, 2024 - G-Transformer for Conditional Average Potential Outcome Estimation over Time
Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
arXiv, 2024 - Conformal Prediction for Causal Effects of Continuous Treatments
Maresa Schröder, Dennis Frauen, Jonas Schweisthal, Konstantin Heß, Valentyn Melnychuk, Stefan Feuerriegel
arXiv, 2024 - DiffPO: A causal diffusion model for learning distributions of potential outcomes
Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal, Stefan Feuerriegel
38th Conference on Neural Information Processing Systems (NeurIPS), 2024 - Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS), 2024 - Unified Guidance for Geometry-Conditioned Molecular Generation
Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian Theis, Stephan Günnemann
38th Conference on Neural Information Processing Systems (NeurIPS), 2024 - Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation
Jiajun Wang, Morteza Ghahremani, Yitong Li, Björn Ommer, Christian Wachinger
Conference on Neural Information Processing Systems (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
Conference on Neural Information Processing Systems (NeurIPS)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 - Gaussian Process-Based Representation Learning via Timeseries Symmetries
Petar Bevanda, Max Beier, Armin Lederer, Alexandre Capone, Stefan Georg Sosnowski, Sandra Hirche
International Conference on Machine Learning (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
International Conference on Learning Representations (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
Conference on Uncertainty in Artificial Intelligence (UAI), 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 - Assessing Robustness via Score-based Adversarial Image Generation
Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann
Transactions on Machine Learning, 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 - 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, 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 - Robust identifiability for symbolic recovery of differential equations
Hillary Hauger, Philipp Scholl, Gitta Kutyniok
ICASSP 2025, 2024 - Second-Order Uncertainty Quantification: A Distance-Based Approach
Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier
41st International Conference on Machine Learning (ICML), 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 - Non-Parametric Representation Learning with Kernels
Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24) 2024, 2024 - 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), 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 - Reconciling privacy and accuracy in AI for medical imaging
Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
Nature Machine Intelligence, 2024 - Ethnic Classifications in Algorithmic Fairness: Concepts, Measures and Implications in Practice
Sofia Jaime, Christoph Kern
2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT, 2024 - Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk Assessment
Maurice Amiriparian Gerczuk, Shahin , Justina Lutz, Wolfgang Papazova Strube, Irina Hasan, Alkomiet , Björn W. Schuller
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2024 - An Automated Evaluation Framework for Graph Database Query Generation Leveraging Large Language Models
Bailan He, Yushan Liu, Marcel Hildebrandt, Zifeng Ding, Yaomengxi Han, Volker Tresp
CEUR Workshop Proceedings, 2024 - This Paper Had the Smartest Reviewers - Flattery Detection Utilising an Audio-Textual Transformer-Based Approach
Lucas Christ, Shahin Amiriparian, Friederike Hawighorst, Ann-Kathrin Schill, Angelo Boutalikakis, Lorenz Graf-Vlachy, Andreas König, Björn W. Schuller
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2024 - Computability of optimizers for AI and data science
Yunseok Lee, Holger Boche, Gitta Kutyniok
Handbook of Numerical Analysis, 2024 - Computing-Model and Computing-Hardware Selection for ICT Under Societal and Judicial Constraints
Yannik N Böck, Holger Boche, Frank H. P. Fitzek, Gitta Kutyniok
IEEE Access, 2024 - On Prompt Sensitivity of ChatGPT in Affective Computing
Mostafa M. Amin, Björn W. Schuller
12th International Conference on Affective Computing and Intelligent Interaction (ACII), 2024 - Learning-based adaption of robotic friction models
Philipp Scholl, Maged Iskandar, Sebastian Wolf, Jinoh Lee, Aras Bacho, Alexander Dietrich, Alin Albu-Schäffer, Gitta Kutyniok
Robotics and Computer-Integrated Manufacturing, 2024 - Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks
Stefan Fischer, Lina Felsner, Richard Osuala, Johannes Kiechle, Daniel Lang, Jan Peeken, Julia Schnabel
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024 - (Predictable) performance bias in unsupervised anomaly detection
Felix Meissen, Svenja Breuer, Moritz A. Knolle, Alena Buyx, Ruth Müller, Georgios Kaissis, Benedikt Wiestler, Daniel Rückert
eBioMedicine, 2024 - Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning
Lukas Christ, Shahin Amiriparian, Manuel Milling, Ilhan Aslan, Björn W. Schuller
Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2024 - Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning
Raffaele Paolino, Sohir Maskey, Pascal Welke, Gitta Kutyniok
Advances in Neural Information Processing Systems, 2024 - A Mathematical Framework for Computability Aspects of Algorithmic Transparency
Holger Boche, Adalbert Fono, Gitta Kutyniok
IEEE International Symposium on Information Theory - Proceedings, 2024 - Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems
Sihua Zhang, Di-Hua Zhai, Xiaobing Dai, Tzu-yuan Huang, Yuanqing Xia, Sandra Hirche
Proceedings of the IEEE Conference on Decision and Control, 2024 - Memorisation in Machine Learning: A Survey of Results
Dmitrii Usynin, Moritz Knolle, Georgios Kaissis
Transactions on Machine Learning Research, 2024 - Privacy for Groups Online: Context Matters
Madiha Zahrar Choksi, Ero Balsa, Frauke Kreuter, Helen Nissenbaum
Proceedings of the ACM on Human-Computer Interaction, 2024 - Towards Multimodal Prediction of Spontaneous Humor: A Novel Dataset and First Results
Lukas Christ, Shahin Amiriparian, Alexander Kathan, Niklas Muller, Andreas Konig, Björn W. Schuller
IEEE Transactions on Affective Computing, 2024 - Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma
Aristeidis Papargyriou, Mulham Najajreh, David P. Cook, Carlo H. Maurer, Stefanie Bärthel, Hendrik A. Messal, Sakthi K. Ravichandran, Till Richter, Moritz Knolle, Thomas Metzler, Akul R. Shastri, Rupert Öllinger, Jacob Jasper, Laura Schmidleitner, Surui Wang, Christian Schneeweis, Hellen Ishikawa-Ankerhold, Thomas Engleitner, Laura Mataite, Mariia Semina, Hussein Trabulssi, Sebastian Lange, Aashreya Ravichandra, Maximilian Schuster, Sebastian Mueller, Katja Peschke, Arlett Schäfer, Sophie Dobiasch, Stephanie E. Combs, Roland M. Schmid, Andreas R. Bausch, Rickmer Braren, Irina Heid, Christina H. Scheel, Günter Schneider, Anja Zeigerer, Malte D. Luecken, Katja Steiger, Georgios Kaissis, Jacco van Rheenen, Fabian J. Theis, Dieter Saur, Roland Rad, Maximilian Reichert
Nature Biomedical Engineering, 2024
2024
- Learning Confident Classifiers in the Presence of Label Noise
Asma Ahmed Hashmi, Aigerim Zhumabayeva, Nikita Kotelevskii, Artem Agafonov, Mohammad Yaqub, Maxim Panov, Martin Takáč
SIAM SDM, 2023 - 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 - 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 - 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 - 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 - 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 - 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
Conference on Neural Information Processing Systems (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 - Sharp Bounds for Generalized Causal Sensitivity Analysis
Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 - Reliable Off-Policy Learning for Dosage Combinations
Jonas Schweisthal, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 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
International Conference on Machine Learning (ICML), 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 - Neural (Tangent Kernel) Collapse
Mariia Seleznova, Dana Weitzner, Raja Giryes, Gitta Kutyniok, Hung-Hsu Chou
Advances in Neural Information Processing Systems (NeurIPS), 2023 - Sumformer: Universal Approximation for Efficient Transformers
Silas Alberti, Niclas Dern, Laura Thesing, Gitta Kutyniok
Proceedings of Machine Learning Research, 2023 - Transferability of graph neural networks: An extended graphon approach
Sohir Maskey, Ron Levie, Gitta Kutyniok
Applied and Computational Harmonic Analysis, 2023 - A Fractional Graph Laplacian Approach to Oversmoothing
Sohir Maskey, Raffaele Paolino, Aras Bacho, Gitta Kutyniok
Advances in Neural Information Processing Systems (NeurIPS), 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 - OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
Yangze Zhou, Gitta Kutyniok, Bruno Ribeiro
Advances in Neural Information Processing Systems (NeurIPS), 2022 - Graph Scattering beyond Wavelet Shackles
Christian Koke, Gitta Kutyniok
Advances in Neural Information Processing Systems (NeurIPS), 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 - Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
Sohir Maskey, Ron Levie, Yunseok Lee, Gitta Kutyniok
Advances in Neural Information Processing Systems (NeurIPS), 2022
2022