Günnemann, Stephan

151 publications

ICLR 2025 A Probabilistic Perspective on Unlearning and Alignment for Large Language Models Yan Scholten, Stephan Günnemann, Leo Schwinn

TMLR 2025 A Unified Approach Towards Active Learning and Out-of-Distribution Detection Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann

TMLR 2025 Adversarial Robustness of Graph Transformers Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann

FnTML 2025 Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems 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, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, 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 J. 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

NeurIPS 2025 Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models Michael Plainer, Hao Wu, Leon Klein, Stephan Günnemann, Frank Noe

ICML 2025 Efficient Time Series Processing for Transformers and State-Space Models Through Token Merging Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn

ICML 2025 Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation Alessandro Palma, Sergei Rybakov, Leon Hetzel, Stephan Günnemann, Fabian J Theis

ICLR 2025 Exact Certification of (Graph) Neural Networks Against Label Poisoning Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar

ICLRW 2025 Exact Certification of (Graph) Neural Networks Against Label Poisoning Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar

ICLRW 2025 Fast Proxies for LLM Robustness Evaluation Tim Beyer, Jan Schuchardt, Leo Schwinn, Stephan Günnemann

WACV 2025 Finding DINO: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Günnemann

ICLR 2025 Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting Marcel Kollovieh, Marten Lienen, David Lüdke, Leo Schwinn, Stephan Günnemann

ICCV 2025 GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation Phillip Mueller, Talip Uenlue, Sebastian Schmidt, Marcel Kollovieh, Jiajie Fan, Stephan Günnemann, Lars Mikelsons

ICLR 2025 Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance Dominik Fuchsgruber, Tim Postuvan, Stephan Günnemann, Simon Geisler

CVPR 2025 Joint Out-of-Distribution Filtering and Data Discovery Active Learning Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann

NeurIPS 2025 Joint Relational Database Generation via Graph-Conditional Diffusion Models Mohamed Amine Ketata, David Lüdke, Leo Schwinn, Stephan Günnemann

ICLR 2025 Learning Equivariant Non-Local Electron Density Functionals Nicholas Gao, Eike Eberhard, Stephan Günnemann

ICLR 2025 Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann

ICLRW 2025 LipShiFT: A Certifiably Robust Shift-Based Vision Transformer Rohan Menon, Nicola Franco, Stephan Günnemann

ICLR 2025 MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J Theis, Stephan Günnemann

NeurIPS 2025 Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow Kristiyan Sakalyan, Alessandro Palma, Filippo Guerranti, Fabian J Theis, Stephan Günnemann

ICLRW 2025 On Learning Quasi-Lagrangian Turbulence Artur P. Toshev, Teodor Kalinov, Nicholas Gao, Stephan Günnemann, Nikolaus A. Adams

ICCV 2025 Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation Sebastian Schmidt, Julius Koerner, Dominik Fuchsgruber, Stefano Gasperini, Federico Tombari, Stephan Günnemann

ICML 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

TMLR 2025 Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann

ICLR 2025 Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning Yan Scholten, Stephan Günnemann

ICML 2025 REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Vincent Cohen-Addad, Johannes Gasteiger, Stephan Günnemann

ICML 2025 The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence Tom Wollschläger, Jannes Elstner, Simon Geisler, Vincent Cohen-Addad, Stephan Günnemann, Johannes Gasteiger

NeurIPS 2025 TreeGen: A Bayesian Generative Model for Hierarchies Marcel Kollovieh, Nils Fleischmann, Filippo Guerranti, Bertrand Charpentier, Stephan Günnemann

ICML 2025 UnHiPPO: Uncertainty-Aware Initialization for State Space Models Marten Lienen, Abdullah Saydemir, Stephan Günnemann

ICML 2025 Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory Dominik Fuchsgruber, Tom Wollschläger, Johannes Bordne, Stephan Günnemann

ICLR 2025 Unlocking Point Processes Through Point Set Diffusion David Lüdke, Enric Rabasseda Raventós, Marcel Kollovieh, Stephan Günnemann

NeurIPS 2025 What Expressivity Theory Misses: Message Passing Complexity for GNNs Niklas Kemper, Tom Wollschläger, Stephan Günnemann

TMLR 2024 Assessing Robustness via Score-Based Adversarial Image Generation Marcel Kollovieh, Lukas Gosch, Marten Lienen, Yan Scholten, Leo Schwinn, Stephan Günnemann

ICMLW 2024 Attacking Large Language Models with Projected Gradient Descent Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann

NeurIPS 2024 Efficient Adversarial Training in LLMs with Continuous Attacks Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, Leo Schwinn

NeurIPSW 2024 Efficient Time Series Processing for Transformers and State-Space Models Through Token Merging Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn

NeurIPS 2024 Energy-Based Epistemic Uncertainty for Graph Neural Networks Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann

NeurIPS 2024 Expected Probabilistic Hierarchies Marcel Kollovieh, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

ICML 2024 Expressivity and Generalization: Fragment-Biases for Molecular GNNs Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann

NeurIPSW 2024 Extracting Unlearned Information from LLMs with Activation Steering Atakan Seyitoğlu, Aleksei Kuvshinov, Leo Schwinn, Stephan Günnemann

ICLR 2024 From Zero to Turbulence: Generative Modeling for 3D Flow Simulation Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann

UAI 2024 Guaranteeing Robustness Against Real-World Perturbations in Time Series Classification Using Conformalized Randomized Smoothing Nicola Franco, Jakob Spiegelberg, Jeanette Miriam Lorenz, Stephan Günnemann

ICMLW 2024 Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann

NeurIPS 2024 Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations Nicholas Gao, Stephan Günnemann

ICLRW 2024 On Representing Electronic Wave Functions with Sign Equivariant Neural Networks Nicholas Gao, Stephan Günnemann

NeurIPSW 2024 Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann

ICMLW 2024 Relaxing Graph Transformers for Adversarial Attacks Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann

NeurIPS 2024 Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood Rayen Dhahri, Alexander Immer, Betrand Charpentier, Stephan Günnemann, Vincent Fortuin

NeurIPS 2024 Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs Through the Embedding Space Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, Stephan Günnemann

NeurIPS 2024 Spatio-Spectral Graph Neural Networks Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann

ICMLW 2024 Spatio-Spectral Graph Neural Networks Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann

IJCAI 2024 Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract) Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer

CVPRW 2024 Towards Engineered Safe AI with Modular Concept Models Lena Heidemann, Iwo Kurzidem, Maureen Monnet, Karsten Roscher, Stephan Günnemann

ICML 2024 Uncertainty for Active Learning on Graphs Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann

CVPRW 2024 Understanding ReLU Network Robustness Through Test Set Certification Performance Nicola Franco, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann

ICMLW 2024 Unfolding Time: Generative Modeling for Turbulent Flows in 4D Abdullah Saydemir, Marten Lienen, Stephan Günnemann

NeurIPS 2024 Unified Guidance for Geometry-Conditioned Molecular Generation Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian Theis, Stephan Günnemann

NeurIPS 2024 Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan Günnemann

NeurIPS 2023 Add and Thin: Diffusion for Temporal Point Processes David Lüdke, Marin Biloš, Oleksandr Shchur, Marten Lienen, Stephan Günnemann

NeurIPSW 2023 Adversarial Attacks and Defenses in Large Language Models: Old and New Threats Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel

NeurIPS 2023 Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

LoG 2023 Edge Directionality Improves Learning on Heterophilic Graphs Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein

ICML 2023 Ewald-Based Long-Range Message Passing for Molecular Graphs Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann

ICMLW 2023 Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness Francesco Campi, Lukas Gosch, Tom Wollschläger, Yan Scholten, Stephan Günnemann

ICML 2023 Generalizing Neural Wave Functions Nicholas Gao, Stephan Günnemann

NeurIPS 2023 Hierarchical Randomized Smoothing Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

NeurIPSW 2023 Latent Space Simulator for Unveiling Molecular Free Energy Landscapes and Predicting Transition Dynamics Simon Dobers, Hannes Stark, Xiang Fu, Dominique Beaini, Stephan Günnemann

ICLR 2023 Localized Randomized Smoothing for Collective Robustness Certification Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann

ICML 2023 Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion Marin Biloš, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann

NeurIPSW 2023 On the Adversarial Robustness of Graph Contrastive Learning Methods Filippo Guerranti, Zinuo Yi, Anna Starovoit, Rafiq Mazen Kamel, Simon Geisler, Stephan Günnemann

NeurIPSW 2023 Poisoning $\times$ Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks Ege Erdogan, Simon Geisler, Stephan Günnemann

NeurIPS 2023 Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More Jan Schuchardt, Yan Scholten, Stephan Günnemann

ICLR 2023 Revisiting Robustness in Graph Machine Learning Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann

ICLR 2023 Sampling-Free Inference for Ab-Initio Potential Energy Surface Networks Nicholas Gao, Stephan Günnemann

ICLRW 2023 The Power of Motifs as Inductive Bias for Learning Molecular Distributions Johanna Sommer, Leon Hetzel, David Lüdke, Fabian J Theis, Stephan Günnemann

CoRL 2023 Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel

ECML-PKDD 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

ICLRW 2023 Training, Architecture, and Prior for Deterministic Uncertainty Methods Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann

ICML 2023 Transformers Meet Directed Graphs Simon Geisler, Yujia Li, Daniel J Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru

NeurIPSW 2023 Transition Path Sampling with Boltzmann Generator-Based MCMC Moves Michael Plainer, Hannes Stark, Charlotte Bunne, Stephan Günnemann

ICML 2023 Uncertainty Estimation for Molecules: Desiderata and Methods Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann

ICLR 2023 Unveiling the Sampling Density in Non-Uniform Geometric Graphs Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie

ICML 2022 3D Infomax Improves GNNs for Molecular Property Prediction Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió

LoG 2022 A Systematic Evaluation of Node Embedding Robustness Alexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie

ICLR 2022 Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions Nicholas Gao, Stephan Günnemann

NeurIPS 2022 Are Defenses for Graph Neural Networks Robust? Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski

ICLR 2022 Differentiable DAG Sampling Bertrand Charpentier, Simon Kibler, Stephan Günnemann

AAAI 2022 Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks Aleksei Kuvshinov, Daniel Knobloch, Daniel Külzer, Elen Vardanyan, Stephan Günnemann

ICLR 2022 End-to-End Learning of Probabilistic Hierarchies on Graphs Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann

TMLR 2022 GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ward Ulissi, C. Lawrence Zitnick, Abhishek Das

ICLR 2022 Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

LoG 2022 Influence-Based Mini-Batching for Graph Neural Networks Johannes Gasteiger, Chendi Qian, Stephan Günnemann

ICML 2022 Intriguing Properties of Input-Dependent Randomized Smoothing Peter Súkenı́k, Aleksei Kuvshinov, Stephan Günnemann

NeurIPS 2022 Invariance-Aware Randomized Smoothing Certificates Jan Schuchardt, Stephan Günnemann

AAAI 2022 Is It All a Cluster Game? - Exploring Out-of-Distribution Detection Based on Clustering in the Embedding Space Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher, Stephan Günnemann

ICLR 2022 Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks Marten Lienen, Stephan Günnemann

NeurIPSW 2022 Modeling Temporal Data as Continuous Functions with Process Diffusion Marin Biloš, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann

ICLR 2022 Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann

NeurIPS 2022 Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution Leon Hetzel, Simon Boehm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis

ICLRW 2022 Predicting Single-Cell Perturbation Responses for Unseen Drugs Leon Hetzel, Simon Boehm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J Theis

NeurIPS 2022 Randomized Message-Interception Smoothing: Gray-Box Certificates for Graph Neural Networks Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann

NeurIPSW 2022 Revisiting Robustness in Graph Machine Learning Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann

MLJ 2022 Robustness Verification of ReLU Networks via Quadratic Programming Aleksei Kuvshinov, Stephan Günnemann

NeurIPSW 2022 Torchode: A Parallel ODE Solver for PyTorch Marten Lienen, Stephan Günnemann

NeurIPSW 2022 Training Differentially Private Graph Neural Networks with Random Walk Sampling Morgane Ayle, Jan Schuchardt, Lukas Gosch, Daniel Zügner, Stephan Günnemann

CVPRW 2022 Understanding the Role of Weather Data for Earth Surface Forecasting Using a ConvLSTM-Based Model Codrut-Andrei Diaconu, Sudipan Saha, Stephan Günnemann, Xiao Xiang Zhu

ICML 2022 Winning the Lottery Ahead of Time: Efficient Early Network Pruning John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann

AISTATS 2021 Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann

NeurIPSW 2021 3D Infomax Improves GNNs for Molecular Property Prediction Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lio

ICLR 2021 Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann

NeurIPS 2021 Detecting Anomalous Event Sequences with Temporal Point Processes Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann

NeurIPS 2021 Directional Message Passing on Molecular Graphs via Synthetic Coordinates Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann

ICML 2021 Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-Based Models Reliable? Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann

NeurIPS 2021 GemNet: Universal Directional Graph Neural Networks for Molecules Johannes Gasteiger, Florian Becker, Stephan Günnemann

NeurIPS 2021 Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann

ICLR 2021 Language-Agnostic Representation Learning of Source Code from Structure and Context Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann

NeurIPS 2021 Neural Flows: Efficient Alternative to Neural ODEs Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann

IJCAI 2021 Neural Temporal Point Processes: A Review Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann

MLJ 2021 Reachable Sets of Classifiers and Regression Models: (non-)robustness Analysis and Robust Training Anna-Kathrin Kopetzki, Stephan Günnemann

NeurIPS 2021 Robustness of Graph Neural Networks at Scale Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann

ICML 2021 Scalable Normalizing Flows for Permutation Invariant Densities Marin Biloš, Stephan Günnemann

ICML 2021 Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More Johannes Gasteiger, Marten Lienen, Stephan Günnemann

ECCVW 2020 Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection Felippe Schmoeller Roza, Maximilian Henne, Karsten Roscher, Stephan Günnemann

ICLR 2020 Continual Learning with Bayesian Neural Networks for Non-Stationary Data Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann

NeurIPS 2020 Deep Rao-Blackwellised Particle Filters for Time Series Forecasting Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus

ICLR 2020 Directional Message Passing for Molecular Graphs Johannes Klicpera, Janek Groß, Stephan Günnemann

ICML 2020 Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann

NeurIPS 2020 Fast and Flexible Temporal Point Processes with Triangular Maps Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann

ECML-PKDD 2020 Gauss Shift: Density Attractor Clustering Faster than Mean Shift Richard Leibrandt, Stephan Günnemann

ICLR 2020 Intensity-Free Learning of Temporal Point Processes Oleksandr Shchur, Marin Biloš, Stephan Günnemann

NeurIPS 2020 Posterior Network: Uncertainty Estimation Without OOD Samples via Density-Based Pseudo-Counts Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

NeurIPS 2020 Reliable Graph Neural Networks via Robust Aggregation Simon Geisler, Daniel Zügner, Stephan Günnemann

ICLR 2019 Adversarial Attacks on Graph Neural Networks via Meta Learning Daniel Zügner, Stephan Günnemann

IJCAI 2019 Adversarial Attacks on Neural Networks for Graph Data Daniel Zügner, Amir Akbarnejad, Stephan Günnemann

ICML 2019 Adversarial Attacks on Node Embeddings via Graph Poisoning Aleksandar Bojchevski, Stephan Günnemann

NeurIPS 2019 Certifiable Robustness to Graph Perturbations Aleksandar Bojchevski, Stephan Günnemann

NeurIPS 2019 Diffusion Improves Graph Learning Johannes Gasteiger, Stefan Weißenberger, Stephan Günnemann

NeurIPS 2019 Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift Stephan Rabanser, Stephan Günnemann, Zachary Lipton

AAAI 2019 Multi-Source Neural Variational Inference Richard Kurle, Stephan Günnemann, Patrick van der Smagt

ICLR 2019 Predict Then Propagate: Graph Neural Networks Meet Personalized PageRank Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann

NeurIPS 2019 Uncertainty on Asynchronous Time Event Prediction Marin Biloš, Bertrand Charpentier, Stephan Günnemann

AAAI 2018 Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure Aleksandar Bojchevski, Stephan Günnemann

ICLR 2018 Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking Aleksandar Bojchevski, Stephan Günnemann

ICML 2018 NetGAN: Generating Graphs via Random Walks Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

MLJ 2015 MultiClust Special Issue on Discovering, Summarizing and Using Multiple Clusterings Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl, Jennifer G. Dy

AISTATS 2015 Preferential Attachment in Graphs with Affinities Jay Lee, Manzil Zaheer, Stephan Günnemann, Alexander J. Smola

ECML-PKDD 2014 Beyond Blocks: Hyperbolic Community Detection Miguel Araujo, Stephan Günnemann, Gonzalo Mateos, Christos Faloutsos

ECML-PKDD 2011 DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors Stephan Günnemann, Brigitte Boden, Thomas Seidl