Stephan Günnemann

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 279

Top Venues

RWTH Aachen University (PHD)

Education History

RWTH Aachen University

PHD

Publications

Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Günnemann
Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes. CoRR (2024)
Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR (2024)
Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, Stephan Günnemann
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. CoRR (2024)
Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier
Structurally Prune Anything: Any Architecture, Any Framework, Any Time. CoRR (2024)
Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann
From Zero to Turbulence: Generative Modeling for 3D Flow Simulation. ICLR (2024)
Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. CoRR (2024)
Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek El Husseini, David Schinz, Nicolas Lenhart, Bjoern H. Menze, Jan Kirschke, Karsten Roscher, Stephan Günnemann
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes. CoRR (2024)
Zhong Li, Simon Geisler, Yuhang Wang, Stephan Günnemann, Matthijs van Leeuwen
Explainable Graph Neural Networks Under Fire. CoRR (2024)
Nicholas Gao, Stephan Günnemann
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks. CoRR (2024)
Nicholas Gao, Stephan Günnemann
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations. CoRR (2024)
Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann
Expressivity and Generalization: Fragment-Biases for Molecular GNNs. CoRR (2024)
Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann
Relaxing Graph Transformers for Adversarial Attacks. CoRR (2024)
Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann
Energy-based Epistemic Uncertainty for Graph Neural Networks. CoRR (2024)
Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
A Unified Approach Towards Active Learning and Out-of-Distribution Detection. CoRR (2024)
Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann
Attacking Large Language Models with Projected Gradient Descent. CoRR (2024)
Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space. CoRR (2024)
Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, Leo Schwinn
Efficient Adversarial Training in LLMs with Continuous Attacks. CoRR (2024)
Florence Regol, Joud Chataoui, Bertrand Charpentier, Mark Coates, Pablo Piantanida, Stephan Günnemann
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. CoRR (2024)
Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. CoRR (2024)
Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann
Spatio-Spectral Graph Neural Networks. CoRR (2024)
Abdullah Saydemir, Marten Lienen, Stephan Günnemann
Unfolding Time: Generative Modeling for Turbulent Flows in 4D. CoRR (2024)
Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
Uncertainty for Active Learning on Graphs. CoRR (2024)
Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan Günnemann
Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy. CoRR (2024)
Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Hierarchical Randomized Smoothing. CoRR (2023)
Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. CoRR (2023)
Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRR (2023)
Tom Haider, Karsten Roscher, Felippe Schmoeller da Roza, Stephan Günnemann
Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models. AAMAS (2023)
Nicholas Gao, Stephan Günnemann
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. ICLR (2023)
Michael Plainer, Hannes Stärk, Charlotte Bunne, Stephan Günnemann
Transition Path Sampling with Boltzmann Generator-based MCMC Moves. CoRR (2023)
Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly. CoRR (2023)
Nicholas Gao, Stephan Günnemann
Generalizing Neural Wave Functions. CoRR (2023)
Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection. CoRR (2023)
David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann
Add and Thin: Diffusion for Temporal Point Processes. NeurIPS (2023)
Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
Revisiting Robustness in Graph Machine Learning. CoRR (2023)
Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out Of Distribution. AISafety/SafeRL@IJCAI (2023)
Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. QCE (2023)
Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
Revisiting Robustness in Graph Machine Learning. ICLR (2023)
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Adversarial Training for Graph Neural Networks. CoRR (2023)
Jan Schuchardt, Yan Scholten, Stephan Günnemann
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. CoRR (2023)
Nicholas Gao, Stephan Günnemann
Generalizing Neural Wave Functions. ICML (2023)
Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann
Training, Architecture, and Prior for Deterministic Uncertainty Methods. CoRR (2023)
Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger
AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS). CoRR (2023)
Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. IV (2023)
Johanna Sommer, Leon Hetzel, David Lüdke, Fabian J. Theis, Stephan Günnemann
The power of motifs as inductive bias for learning molecular distributions. CoRR (2023)
Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. CoRR (2023)
David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann
Add and Thin: Diffusion for Temporal Point Processes. CoRR (2023)
Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR (2023)
Morgane Ayle, Jan Schuchardt, Lukas Gosch, Daniel Zügner, Stephan Günnemann
Training Differentially Private Graph Neural Networks with Random Walk Sampling. CoRR (2023)
Francesco Campi, Lukas Gosch, Tom Wollschläger, Yan Scholten, Stephan Günnemann
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness. CoRR (2023)
Hao Lin, Hongfu Liu, Junjie Wu, Hong Li, Stephan Günnemann
-Means-based Consensus Clustering. ACM Trans. Math. Softw. 49 (4) (2023)
Johannes Getzner, Bertrand Charpentier, Stephan Günnemann
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. CoRR (2023)
Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J. Theis, Stephan Günnemann
MAGNet: Motif-Agnostic Generation of Molecules from Shapes. CoRR (2023)
Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann
Ewald-based Long-Range Message Passing for Molecular Graphs. CoRR (2023)
Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. CoRR (2023)
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. ECML/PKDD (1) (2023)
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
Edge Directionality Improves Learning on Heterophilic Graphs. CoRR (2023)
Ege Erdogan, Simon Geisler, Stephan Günnemann
Poisoning × Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks. CoRR (2023)
Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann
Localized Randomized Smoothing for Collective Robustness Certification. ICLR (2023)
Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann
Assessing Robustness via Score-Based Adversarial Image Generation. CoRR (2023)
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR (2023)
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
Uncertainty Estimation for Molecules: Desiderata and Methods. ICML (2023)
Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Hierarchical Randomized Smoothing. NeurIPS (2023)
Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. ICBINB (2023)
Marten Lienen, Jan Hansen-Palmus, David Lüdke, Stephan Günnemann
Generative Diffusion for 3D Turbulent Flows. CoRR (2023)
Sebastian Schmidt, Stephan Günnemann
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. BMVC (2023)
Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. ICML (2023)
Jan Schuchardt, Yan Scholten, Stephan Günnemann
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. NeurIPS (2023)
Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. CoRR (2023)
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
Edge Directionality Improves Learning on Heterophilic Graphs. LoG (2023)
Sebastian Schmidt, Stephan Günnemann
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. CoRR (2023)
Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger
Enabling Machine Learning in Software Architecture Frameworks. CAIN (2023)
Richard Leibrandt, Stephan Günnemann
Generalized density attractor clustering for incomplete data. Data Min. Knowl. Discov. 37 (2) (2023)
Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRL (2023)
Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günnemann, Neil Shah, Meng Jiang
Graph Data Augmentation for Graph Machine Learning: A Survey. IEEE Data Eng. Bull. 46 (2) (2023)
Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru
Transformers Meet Directed Graphs. ICML (2023)
Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. CoRR (2023)
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
Uncertainty Estimation for Molecules: Desiderata and Methods. CoRR (2023)
Filippo Guerranti, Zinuo Yi, Anna Starovoit, Rafiq Kamel, Simon Geisler, Stephan Günnemann
On the Adversarial Robustness of Graph Contrastive Learning Methods. CoRR (2023)
Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie
Unveiling the sampling density in non-uniform geometric graphs. ICLR (2023)
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. NeurIPS (2023)
Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann
Ewald-based Long-Range Message Passing for Molecular Graphs. ICML (2023)
Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru
Transformers Meet Directed Graphs. CoRR (2023)
Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski
Are Defenses for Graph Neural Networks Robust? CoRR (2023)
Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann
End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR (2022)
Marten Lienen, Stephan Günnemann
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. CoRR (2022)
Jan Schuchardt, Stephan Günnemann
Invariance-Aware Randomized Smoothing Certificates. CoRR (2022)
Marten Lienen, Stephan Günnemann
torchode: A Parallel ODE Solver for PyTorch. CoRR (2022)
Johannes Gasteiger, Chendi Qian, Stephan Günnemann
Influence-Based Mini-Batching for Graph Neural Networks. LoG (2022)
Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. ICLR (2022)
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió
3D Infomax improves GNNs for Molecular Property Prediction. ICML (2022)
Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. NeurIPS (2022)
Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie
A Systematic Evaluation of Node Embedding Robustness. CoRR (2022)
Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, Johannes T. Margraf
How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? Mach. Learn. Sci. Technol. 3 (4) (2022)
Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. NeurIPS (2022)
Jonathan Külz, Andreas Spitz, Ahmad Abu-Akel, Stephan Günnemann, Robert West
United States Politicians' Tone Became More Negative with 2016 Primary Campaigns. CoRR (2022)
Armin Moin, Ukrit Wattanavaekin, Alexandra Lungu, Moharram Challenger, Atta Badii, Stephan Günnemann
Enabling Automated Machine Learning for Model-Driven AI Engineering. CoRR (2022)
Martin Grohe, Stephan Günnemann, Stefanie Jegelka, Christopher Morris
Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). Dagstuhl Reports 12 (3) (2022)
Nicholas Gao, Stephan Günnemann
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. ICLR (2022)
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary W. Ulissi, C. Lawrence Zitnick, Abhishek Das
How Do Graph Networks Generalize to Large and Diverse Molecular Systems? CoRR (2022)
Aleksei Kuvshinov, Daniel Knobloch, Daniel Külzer, Elen Vardanyan, Stephan Günnemann
Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks. AAAI (2022)

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Publication Activity (10 Years)

Top Topics

Top Venues

Stephan Günnemann