Stephan Günnemann

445 papers A* 111A 19B 15C 2Misc 11Journal 238Unranked 47

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	3D Molecule Generation from Rigid Motifs via SE(3) Flows. CoRR Roman Poletukhin, Marcel Kollovieh, Eike Eberhard, Stephan Günnemann
2026	J	`jnl`	A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness. CoRR Leo Schwinn, Moritz Ladenburger, Tim Beyer, Mehrnaz Mofakhami, Gauthier Gidel, Stephan Günnemann
2026	J	`jnl`	A Foundation Model for Virtual Sensors. CoRR Leon Götz, Lars Frederik Peiss, Erik Sauer, Andreas Udo Sass, Thorsten Bagdonat, Stephan Günnemann, Leo Schwinn
2026	J	`jnl`	Amplified Patch-Level Differential Privacy for Free via Random Cropping. CoRR Kaan Durmaz, Jan Schuchardt, Sebastian Schmidt, Stephan Günnemann
2026	J	`jnl`	Amplified Patch-Level Differential Privacy for Free via Random Cropping. Trans. Mach. Learn. Res. Kaan Durmaz, Jan Schuchardt, Sebastian Schmidt, Stephan Günnemann
2026	J	`jnl`	Closing the Distribution Gap in Adversarial Training for LLMs. CoRR Chengzhi Hu, Jonas Dornbusch, David Lüdke, Stephan Günnemann, Leo Schwinn
2026	J	`jnl`	Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State. CoRR Nicholas Gao, Till Grutschus, Frank Noé, Stephan Günnemann
2026	J	`jnl`	Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics. CoRR Egor Antipov, Alessandro Palma, Lorenzo Consoli, Stephan Günnemann, Andrea Dittadi, Fabian J. Theis
2026	J	`jnl`	Task-Awareness Improves LLM Generations and Uncertainty. CoRR Tim Tomov, Dominik Fuchsgruber, Stephan Günnemann
2025	J	`jnl`	A Machine Learning Perspective on Automated Driving Corner Cases. CoRR Sebastian Schmidt, Julius Körner, Stephan Günnemann
2025	A*	`conf`	A Probabilistic Perspective on Unlearning and Alignment for Large Language Models. ICLR Yan Scholten, Stephan Günnemann, Leo Schwinn
2025	J	`jnl`	A Unified Approach Towards Active Learning and Out-of-Distribution Detection. Trans. Mach. Learn. Res. Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems. CoRR Michael Scherbela, Nicholas Gao, Philipp Grohs, Stephan Günnemann
2025	J	`jnl`	AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Research. CoRR Tim Beyer, Jonas Dornbusch, Jakob Steimle, Moritz Ladenburger, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives. CoRR Leo Schwinn, Yan Scholten, Tom Wollschläger, Sophie Xhonneux, Stephen Casper, Stephan Günnemann, Gauthier Gidel
2025	J	`jnl`	Adversarial Robustness of Graph Transformers. Trans. Mach. Learn. Res. Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Found. Trends Mach. Learn. 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
2025	J	`jnl`	Byte Pair Encoding for Efficient Time Series Forecasting. CoRR Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn
2025	J	`jnl`	Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models. CoRR Michael Plainer, Hao Wu, Leon Klein, Stephan Günnemann, Frank Noé
2025	J	`jnl`	Diffusion LLMs are Natural Adversaries for any LLM. CoRR David Lüdke, Tom Wollschläger, Paul Ungermann, Stephan Günnemann, Leo Schwinn
2025	J	`jnl`	Discrete Bayesian Sample Inference for Graph Generation. CoRR Ole Petersen, Marcel Kollovieh, Marten Lienen, Stephan Günnemann
2025	J	`jnl`	Edit-Based Flow Matching for Temporal Point Processes. CoRR David Lüdke, Marten Lienen, Marcel Kollovieh, Stephan Günnemann
2025	J	`jnl`	Effective Data Pruning through Score Extrapolation. CoRR Sebastian Schmidt, Prasanga Dhungel, Christoffer Löffler, Björn Nieth, Stephan Günnemann, Leo Schwinn
2025	A*	`conf`	Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. ICML Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn
2025	J	`jnl`	Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling. CoRR Niclas Dern, Lennart Redl, Sebastian Pfister, Marcel Kollovieh, David Lüdke, Stephan Günnemann
2025	A*	`conf`	Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation. ICML Alessandro Palma, Sergei Rybakov, Leon Hetzel, Stephan Günnemann, Fabian J. Theis
2025	J	`jnl`	Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation. CoRR Alessandro Palma, Sergei Rybakov, Leon Hetzel, Stephan Günnemann, Fabian J. Theis
2025	J	`jnl`	Evaluating the transferability of adversarial robustness to target domains. Knowl. Inf. Syst. Anna-Kathrin Kopetzki, Aleksandar Bojchevski, Stephan Günnemann
2025	A*	`conf`	Exact Certification of (Graph) Neural Networks Against Label Poisoning. ICLR Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar
2025	J	`jnl`	Fast Proxies for LLM Robustness Evaluation. CoRR Tim Beyer, Jan Schuchardt, Leo Schwinn, Stephan Günnemann
2025	A	`conf`	Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes. WACV Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Günnemann
2025	A*	`conf`	Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting. ICLR Marcel Kollovieh, Marten Lienen, David Lüdke, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	Generative Modeling with Bayesian Sample Inference. CoRR Marten Lienen, Marcel Kollovieh, Stephan Günnemann
2025	J	`jnl`	GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation. CoRR Phillip Mueller, Talip Uenlue, Sebastian Schmidt, Marcel Kollovieh, Jiajie Fan, Stephan Günnemann, Lars Mikelsons
2025	A*	`conf`	Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance. ICLR Dominik Fuchsgruber, Tim Postuvan, Stephan Günnemann, Simon Geisler
2025	A*	`conf`	Joint Out-of-Distribution Filtering and Data Discovery Active Learning. CVPR Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	Joint Out-of-Distribution Filtering and Data Discovery Active Learning. CoRR Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	Joint Relational Database Generation via Graph-Conditional Diffusion Models. CoRR Mohamed Amine Ketata, David Lüdke, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	LLM-Safety Evaluations Lack Robustness. CoRR Tim Beyer, Sophie Xhonneux, Simon Geisler, Gauthier Gidel, Leo Schwinn, Stephan Günnemann
2025	A*	`conf`	Learning Equivariant Non-Local Electron Density Functionals. ICLR Nicholas Gao, Eike Eberhard, Stephan Günnemann
2025	A*	`conf`	Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space. ICLR Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann
2025	J	`jnl`	LipShiFT: A Certifiably Robust Shift-based Vision Transformer. CoRR Rohan Menon, Nicola Franco, Stephan Günnemann
2025	J	`jnl`	Long-Range Graph Wavelet Networks. CoRR Filippo Guerranti, Fabrizio Forte, Simon Geisler, Stephan Günnemann
2025	A*	`conf`	MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds. ICLR Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J. Theis, Stephan Günnemann
2025	J	`jnl`	Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs. CoRR Yan Scholten, Sophie Xhonneux, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow. CoRR Kristiyan Sakalyan, Alessandro Palma, Filippo Guerranti, Fabian J. Theis, Stephan Günnemann
2025	J	`jnl`	OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders. PLoS Comput. Biol. Klemens Flöge, Srisruthi Udayakumar, Johanna Sommer, Marie Piraud, Stefan Kesselheim, Vincent Fortuin, Stephan Günnemann, Karel J. van der Weg, Holger Gohlke, Erinc Merdivan, Alina Bazarova
2025	J	`jnl`	Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation. CoRR Johanna Sommer, John Rachwan, Nils Fleischmann, Stephan Günnemann, Bertrand Charpentier
2025	J	`jnl`	Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation. CoRR Sebastian Schmidt, Julius Körner, Dominik Fuchsgruber, Stefano Gasperini, Federico Tombari, Stephan Günnemann
2025	A*	`conf`	Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting. ICML Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
2025	J	`jnl`	Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting. CoRR Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
2025	J	`jnl`	Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. Trans. Mach. Learn. Res. Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann
2025	A*	`conf`	Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning. ICLR Yan Scholten, Stephan Günnemann
2025	A*	`conf`	REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective. ICML Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Vincent Cohen-Addad, Johannes Gasteiger, Stephan Günnemann
2025	J	`jnl`	REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective. CoRR Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Vincent Cohen-Addad, Johannes Gasteiger, Stephan Günnemann
2025	J	`jnl`	Rethinking Link Prediction for Directed Graphs. CoRR Mingguo He, Yuhe Guo, Yanping Zheng, Zhewei Wei, Stephan Günnemann, Xiaokui Xiao
2025	J	`jnl`	SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation. CoRR Rafiq Kamel, Filippo Guerranti, Simon Geisler, Stephan Günnemann
2025	J	`jnl`	Scalable Object Detection in the Car Interior With Vision Foundation Models. CoRR Bálint Mészáros, Ahmet Firintepe, Sebastian Schmidt, Stephan Günnemann
2025	J	`jnl`	Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking. CoRR Tim Beyer, Yan Scholten, Leo Schwinn, Stephan Günnemann
2025	J	`jnl`	Targeted AMP generation through controlled diffusion with efficient embeddings. CoRR Diogo Soares, Leon Hetzel, Paulina Szymczak, Fabian J. Theis, Stephan Günnemann, Ewa Szczurek
2025	A*	`conf`	The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence. ICML Tom Wollschläger, Jannes Elstner, Simon Geisler, Vincent Cohen-Addad, Stephan Günnemann, Johannes Gasteiger
2025	J	`jnl`	The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence. CoRR Tom Wollschläger, Jannes Elstner, Simon Geisler, Vincent Cohen-Addad, Stephan Günnemann, Johannes Gasteiger
2025	J	`jnl`	The Illusion of Certainty: Uncertainty quantification for LLMs fails under ambiguity. CoRR Tim Tomov, Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann
2025	A*	`conf`	UnHiPPO: Uncertainty-aware Initialization for State Space Models. ICML Marten Lienen, Abdullah Saydemir, Stephan Günnemann
2025	J	`jnl`	UnHiPPO: Uncertainty-aware Initialization for State Space Models. CoRR Marten Lienen, Abdullah Saydemir, Stephan Günnemann
2025	A*	`conf`	Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory. ICML Dominik Fuchsgruber, Tom Wollschläger, Johannes Bordne, Stephan Günnemann
2025	J	`jnl`	Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory. CoRR Dominik Fuchsgruber, Tom Wollschläger, Johannes Bordne, Stephan Günnemann
2025	J	`jnl`	Unexplored flaws in multiple-choice VQA evaluations. CoRR Fabio Rosenthal, Sebastian Schmidt, Thorsten Graf, Thorsten Bagodonat, Stephan Günnemann, Leo Schwinn
2025	J	`jnl`	Unified Guidance for Geometry-Conditioned Molecular Generation. CoRR Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian J. Theis, Stephan Günnemann
2025	A*	`conf`	Unlocking Point Processes through Point Set Diffusion. ICLR David Lüdke, Enric Rabasseda Raventós, Marcel Kollovieh, Stephan Günnemann
2025	J	`jnl`	What Expressivity Theory Misses: Message Passing Complexity for GNNs. CoRR Niklas Kemper, Tom Wollschläger, Stephan Günnemann
2025	—	`conf`	Zero-Shot Open-Vocabulary OOD Object Detection and Grounding using Vision Language Models. NLDL Poulami Sinhamahapatra, Shirsha Bose, Karsten Roscher, Stephan Günnemann
2024	J	`jnl`	A Probabilistic Perspective on Unlearning and Alignment for Large Language Models. CoRR Yan Scholten, Stephan Günnemann, Leo Schwinn
2024	J	`jnl`	A Unified Approach Towards Active Learning and Out-of-Distribution Detection. CoRR Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
2024	J	`jnl`	Assessing Robustness via Score-Based Adversarial Image Generation. Trans. Mach. Learn. Res. Marcel Kollovieh, Lukas Gosch, Marten Lienen, Yan Scholten, Leo Schwinn, Stephan Günnemann
2024	J	`jnl`	Attacking Large Language Models with Projected Gradient Descent. CoRR Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann
2024	Misc	`conf`	Certifiably Robust Encoding Schemes. QCE Aman Saxena, Tom Wollschläger, Nicola Franco, Jeanette Miriam Lorenz, Stephan Günnemann
2024	J	`jnl`	Certifiably Robust Encoding Schemes. CoRR Aman Saxena, Tom Wollschläger, Nicola Franco, Jeanette Miriam Lorenz, Stephan Günnemann
2024	A	`conf`	Deep Sensor Fusion with Constraint Safety Bounds for High Precision Localization. IROS Sebastian Schmidt, Ludwig Stumpp, Diego Valverde, Stephan Günnemann
2024	Misc	`conf`	Discrete Randomized Smoothing Meets Quantum Computing. QCE Tom Wollschläger, Aman Saxena, Nicola Franco, Jeanette Miriam Lorenz, Stephan Günnemann
2024	J	`jnl`	Discrete Randomized Smoothing Meets Quantum Computing. CoRR Tom Wollschläger, Aman Saxena, Nicola Franco, Jeanette Miriam Lorenz, Stephan Günnemann
2024	A*	`conf`	Efficient Adversarial Training in LLMs with Continuous Attacks. NeurIPS Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, Leo Schwinn
2024	J	`jnl`	Efficient Adversarial Training in LLMs with Continuous Attacks. CoRR Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, Leo Schwinn
2024	J	`jnl`	Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. CoRR Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn
2024	A*	`conf`	Energy-based Epistemic Uncertainty for Graph Neural Networks. NeurIPS Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann
2024	J	`jnl`	Energy-based Epistemic Uncertainty for Graph Neural Networks. CoRR Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann
2024	J	`jnl`	Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes. CoRR Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek El Husseini, David Schinz, Nicolas Lenhart, Bjoern H. Menze, Jan Kirschke, Karsten Roscher, Stephan Günnemann
2024	J	`jnl`	Exact Certification of (Graph) Neural Networks Against Label Poisoning. CoRR Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar
2024	A*	`conf`	Expected Probabilistic Hierarchies. NeurIPS Marcel Kollovieh, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2024	J	`jnl`	Explainable Graph Neural Networks Under Fire. CoRR Zhong Li, Simon Geisler, Yuhang Wang, Stephan Günnemann, Matthijs van Leeuwen
2024	A*	`conf`	Expressivity and Generalization: Fragment-Biases for Molecular GNNs. ICML Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann
2024	J	`jnl`	Expressivity and Generalization: Fragment-Biases for Molecular GNNs. CoRR Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann
2024	J	`jnl`	Extracting Unlearned Information from LLMs with Activation Steering. CoRR Atakan Seyitoglu, Aleksei Kuvshinov, Leo Schwinn, Stephan Günnemann
2024	J	`jnl`	Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes. CoRR Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Günnemann
2024	J	`jnl`	Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting. CoRR Marcel Kollovieh, Marten Lienen, David Lüdke, Leo Schwinn, Stephan Günnemann
2024	A*	`conf`	From Zero to Turbulence: Generative Modeling for 3D Flow Simulation. ICLR Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann
2024	J	`jnl`	Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems. IEEE Robotics Autom. Lett. Sebastian Schmidt, Lukas Stappen, Leo Schwinn, Stephan Günnemann
2024	J	`jnl`	Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance. CoRR Dominik Fuchsgruber, Tim Postuvan, Stephan Günnemann, Simon Geisler
2024	J	`jnl`	Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy. CoRR Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan Günnemann
2024	A	`conf`	Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing. UAI Nicola Franco, Jakob Spiegelberg, Jeanette Miriam Lorenz, Stephan Günnemann
2024	J	`jnl`	Learning Equivariant Non-Local Electron Density Functionals. CoRR Nicholas Gao, Eike Eberhard, Stephan Günnemann
2024	J	`jnl`	Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space. CoRR Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann
2024	A*	`conf`	Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations. NeurIPS Nicholas Gao, Stephan Günnemann
2024	J	`jnl`	Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations. CoRR Nicholas Gao, Stephan Günnemann
2024	J	`jnl`	On Representing Electronic Wave Functions with Sign Equivariant Neural Networks. CoRR Nicholas Gao, Stephan Günnemann
2024	J	`jnl`	OneProt: Towards Multi-Modal Protein Foundation Models. CoRR Klemens Flöge, Srisruthi Udayakumar, Johanna Sommer, Marie Piraud, Stefan Kesselheim, Vincent Fortuin, Stephan Günnemann, Karel J. van der Weg, Holger Gohlke, Alina Bazarova, Erinc Merdivan
2024	J	`jnl`	Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. CoRR Florence Regol, Joud Chataoui, Bertrand Charpentier, Mark Coates, Pablo Piantanida, Stephan Günnemann
2024	J	`jnl`	Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. CoRR Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann
2024	J	`jnl`	Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning. CoRR Yan Scholten, Stephan Günnemann
2024	J	`jnl`	Relaxing Graph Transformers for Adversarial Attacks. CoRR Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann
2024	J	`jnl`	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
2024	A*	`conf`	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. NeurIPS Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
2024	A*	`conf`	Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. NeurIPS Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, Stephan Günnemann
2024	J	`jnl`	Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. CoRR Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, Stephan Günnemann
2024	A*	`conf`	Spatio-Spectral Graph Neural Networks. NeurIPS Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann
2024	J	`jnl`	Spatio-Spectral Graph Neural Networks. CoRR Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann
2024	J	`jnl`	Structurally Prune Anything: Any Architecture, Any Framework, Any Time. CoRR Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier
2024	A*	`conf`	Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). IJCAI Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2024	—	`conf`	Towards Engineered Safe AI with Modular Concept Models. CVPR Workshops Lena Heidemann, Iwo Kurzidem, Maureen Monnet, Karsten Roscher, Stephan Günnemann
2024	A*	`conf`	Uncertainty for Active Learning on Graphs. ICML Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
2024	J	`jnl`	Uncertainty for Active Learning on Graphs. CoRR Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
2024	—	`conf`	Understanding ReLU Network Robustness Through Test Set Certification Performance. CVPR Workshops Nicola Franco, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann
2024	J	`jnl`	Unfolding Time: Generative Modeling for Turbulent Flows in 4D. CoRR Abdullah Saydemir, Marten Lienen, Stephan Günnemann
2024	A*	`conf`	Unified Guidance for Geometry-Conditioned Molecular Generation. NeurIPS Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian J. Theis, Stephan Günnemann
2024	A*	`conf`	Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification. NeurIPS Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan Günnemann
2024	J	`jnl`	Unlocking Point Processes through Point Set Diffusion. CoRR David Lüdke, Enric Rabasseda Raventós, Marcel Kollovieh, Stephan Günnemann
2023	A*	`conf`	(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. NeurIPS Jan Schuchardt, Yan Scholten, Stephan Günnemann
2023	J	`jnl`	(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. CoRR Jan Schuchardt, Yan Scholten, Stephan Günnemann
2023	J	`jnl`	AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS). CoRR Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger
2023	J	`jnl`	Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. CoRR Johannes Getzner, Bertrand Charpentier, Stephan Günnemann
2023	A*	`conf`	Add and Thin: Diffusion for Temporal Point Processes. NeurIPS David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann
2023	J	`jnl`	Add and Thin: Diffusion for Temporal Point Processes. CoRR David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann
2023	—	`conf`	Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. ICBINB Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel
2023	J	`jnl`	Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. CoRR Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel
2023	J	`jnl`	Adversarial Training for Graph Neural Networks. CoRR Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2023	A*	`conf`	Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. NeurIPS Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2023	J	`jnl`	Algorithm 1038: KCC: A MATLAB Package for ACM Trans. Math. Softw. Hao Lin, Hongfu Liu, Junjie Wu, Hong Li, Stephan Günnemann
2023	J	`jnl`	Are Defenses for Graph Neural Networks Robust? CoRR Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski
2023	J	`jnl`	Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR 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 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
2023	J	`jnl`	Assessing Robustness via Score-Based Adversarial Image Generation. CoRR Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann
2023	J	`jnl`	Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. CoRR Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann
2023	J	`jnl`	Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly. CoRR Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel
2023	—	`conf`	Diffusion Denoised Smoothing for Certified and Adversarial Robust Out Of Distribution. AISafety/SafeRL@IJCAI Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann
2023	J	`jnl`	Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection. CoRR Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann
2023	—	`conf`	Edge Directionality Improves Learning on Heterophilic Graphs. LoG Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
2023	J	`jnl`	Edge Directionality Improves Learning on Heterophilic Graphs. CoRR Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
2023	Misc	`conf`	Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. QCE Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz
2023	J	`jnl`	Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. CoRR Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz
2023	B	`conf`	Enabling Machine Learning in Software Architecture Frameworks. CAIN Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger
2023	A*	`conf`	Ewald-based Long-Range Message Passing for Molecular Graphs. ICML Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann
2023	J	`jnl`	Ewald-based Long-Range Message Passing for Molecular Graphs. CoRR Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann
2023	J	`jnl`	Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness. CoRR Francesco Campi, Lukas Gosch, Tom Wollschläger, Yan Scholten, Stephan Günnemann
2023	J	`jnl`	Generalized density attractor clustering for incomplete data. Data Min. Knowl. Discov. Richard Leibrandt, Stephan Günnemann
2023	A*	`conf`	Generalizing Neural Wave Functions. ICML Nicholas Gao, Stephan Günnemann
2023	J	`jnl`	Generalizing Neural Wave Functions. CoRR Nicholas Gao, Stephan Günnemann
2023	J	`jnl`	Generative Diffusion for 3D Turbulent Flows. CoRR Marten Lienen, Jan Hansen-Palmus, David Lüdke, Stephan Günnemann
2023	J	`jnl`	Graph Data Augmentation for Graph Machine Learning: A Survey. IEEE Data Eng. Bull. Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günnemann, Neil Shah, Meng Jiang
2023	A*	`conf`	Hierarchical Randomized Smoothing. NeurIPS Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
2023	J	`jnl`	Hierarchical Randomized Smoothing. CoRR Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
2023	A*	`conf`	Localized Randomized Smoothing for Collective Robustness Certification. ICLR Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann
2023	J	`jnl`	MAGNet: Motif-Agnostic Generation of Molecules from Shapes. CoRR Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J. Theis, Stephan Günnemann
2023	A*	`conf`	Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. ICML Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
2023	J	`jnl`	On the Adversarial Robustness of Graph Contrastive Learning Methods. CoRR Filippo Guerranti, Zinuo Yi, Anna Starovoit, Rafiq Kamel, Simon Geisler, Stephan Günnemann
2023	A	`conf`	Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models. AAMAS Tom Haider, Karsten Roscher, Felippe Schmoeller da Roza, Stephan Günnemann
2023	J	`jnl`	Poisoning × Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks. CoRR Ege Erdogan, Simon Geisler, Stephan Günnemann
2023	C	`conf`	Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. IV Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann
2023	J	`jnl`	Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. CoRR Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann
2023	J	`jnl`	Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. CoRR Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
2023	A*	`conf`	Revisiting Robustness in Graph Machine Learning. ICLR Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
2023	J	`jnl`	Revisiting Robustness in Graph Machine Learning. CoRR Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
2023	A*	`conf`	Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. ICLR Nicholas Gao, Stephan Günnemann
2023	A	`conf`	Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. BMVC Sebastian Schmidt, Stephan Günnemann
2023	J	`jnl`	Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. CoRR Sebastian Schmidt, Stephan Günnemann
2023	J	`jnl`	The power of motifs as inductive bias for learning molecular distributions. CoRR Johanna Sommer, Leon Hetzel, David Lüdke, Fabian J. Theis, Stephan Günnemann
2023	Misc	`conf`	Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRL Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel
2023	J	`jnl`	Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRR Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel
2023	—	`conf`	Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. ECML/PKDD (1) Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023	J	`jnl`	Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023	J	`jnl`	Training Differentially Private Graph Neural Networks with Random Walk Sampling. CoRR Morgane Ayle, Jan Schuchardt, Lukas Gosch, Daniel Zügner, Stephan Günnemann
2023	J	`jnl`	Training, Architecture, and Prior for Deterministic Uncertainty Methods. CoRR Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann
2023	A*	`conf`	Transformers Meet Directed Graphs. ICML Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru
2023	J	`jnl`	Transformers Meet Directed Graphs. CoRR Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru
2023	J	`jnl`	Transition Path Sampling with Boltzmann Generator-based MCMC Moves. CoRR Michael Plainer, Hannes Stärk, Charlotte Bunne, Stephan Günnemann
2023	A*	`conf`	Uncertainty Estimation for Molecules: Desiderata and Methods. ICML Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
2023	J	`jnl`	Uncertainty Estimation for Molecules: Desiderata and Methods. CoRR Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
2023	A*	`conf`	Unveiling the sampling density in non-uniform geometric graphs. ICLR Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie
2022	A*	`conf`	3D Infomax improves GNNs for Molecular Property Prediction. ICML Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió
2022	—	`conf`	A Systematic Evaluation of Node Embedding Robustness. LoG Alexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie
2022	J	`jnl`	A Systematic Evaluation of Node Embedding Robustness. CoRR Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie
2022	J	`jnl`	A model-driven approach to machine learning and software modeling for the IoT. Softw. Syst. Model. Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann
2022	A*	`conf`	Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. ICLR Nicholas Gao, Stephan Günnemann
2022	A*	`conf`	Are Defenses for Graph Neural Networks Robust? NeurIPS Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski
2022	J	`jnl`	C.DOT - Convolutional Deep Object Tracker for Augmented Reality Based Purely on Synthetic Data. IEEE Trans. Vis. Comput. Graph. Kevin Kennard Thiel, Florian Naumann, Eduard Jundt, Stephan Günnemann, Gudrun Klinker
2022	A*	`conf`	Differentiable DAG Sampling. ICLR Bertrand Charpentier, Simon Kibler, Stephan Günnemann
2022	J	`jnl`	Differentiable DAG Sampling. CoRR Bertrand Charpentier, Simon Kibler, Stephan Günnemann
2022	J	`jnl`	Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning. CoRR Bertrand Charpentier, Ransalu Senanayake, Mykel J. Kochenderfer, Stephan Günnemann
2022	A*	`conf`	Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks. AAAI Aleksei Kuvshinov, Daniel Knobloch, Daniel Külzer, Elen Vardanyan, Stephan Günnemann
2022	J	`jnl`	Enabling Automated Machine Learning for Model-Driven AI Engineering. CoRR Armin Moin, Ukrit Wattanavaekin, Alexandra Lungu, Moharram Challenger, Atta Badii, Stephan Günnemann
2022	A*	`conf`	End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann
2022	J	`jnl`	GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets. Trans. Mach. Learn. Res. Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary W. Ulissi, C. Lawrence Zitnick, Abhishek Das
2022	A*	`conf`	Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. ICLR Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
2022	J	`jnl`	Graph Data Augmentation for Graph Machine Learning: A Survey. CoRR Tong Zhao, Gang Liu, Stephan Günnemann, Meng Jiang
2022	J	`jnl`	Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). Dagstuhl Reports Martin Grohe, Stephan Günnemann, Stefanie Jegelka, Christopher Morris
2022	J	`jnl`	How Do Graph Networks Generalize to Large and Diverse Molecular Systems? CoRR Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary W. Ulissi, C. Lawrence Zitnick, Abhishek Das
2022	J	`jnl`	How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? Mach. Learn. Sci. Technol. Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, Johannes T. Margraf
2022	—	`conf`	Influence-Based Mini-Batching for Graph Neural Networks. LoG Johannes Gasteiger, Chendi Qian, Stephan Günnemann
2022	J	`jnl`	Influence-Based Mini-Batching for Graph Neural Networks. CoRR Johannes Gasteiger, Chendi Qian, Stephan Günnemann
2022	A*	`conf`	Intriguing Properties of Input-Dependent Randomized Smoothing. ICML Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann
2022	A*	`conf`	Invariance-Aware Randomized Smoothing Certificates. NeurIPS Jan Schuchardt, Stephan Günnemann
2022	J	`jnl`	Invariance-Aware Randomized Smoothing Certificates. CoRR Jan Schuchardt, Stephan Günnemann
2022	J	`jnl`	Irregularly-Sampled Time Series Modeling with Spline Networks. CoRR Marin Bilos, Emanuel Ramneantu, Stephan Günnemann
2022	—	`conf`	Is it all a cluster game? - Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space. SafeAI@AAAI Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher, Stephan Günnemann
2022	J	`jnl`	Is it all a cluster game? - Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space. CoRR Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher, Stephan Günnemann
2022	A*	`conf`	Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. ICLR Marten Lienen, Stephan Günnemann
2022	J	`jnl`	Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. CoRR Marten Lienen, Stephan Günnemann
2022	J	`jnl`	Localized Randomized Smoothing for Collective Robustness Certification. CoRR Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann
2022	J	`jnl`	MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat. CoRR Jörg Christian Kirchhof, Evgeny Kusmenko, Jonas Ritz, Bernhard Rumpe, Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger
2022	—	`conf`	MDE for machine learning-enabled software systems: a case study and comparison of MontiAnna & ML-Quadrat. MoDELS (Companion) Jörg Christian Kirchhof, Evgeny Kusmenko, Jonas Ritz, Bernhard Rumpe, Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger
2022	—	`conf`	ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. ICSE-Companion Armin Moin, Andrei Mituca, Moharram Challenger, Atta Badii, Stephan Günnemann
2022	J	`jnl`	Modeling Temporal Data as Continuous Functions with Process Diffusion. CoRR Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
2022	J	`jnl`	Multi-Objective Model Selection for Time Series Forecasting. CoRR Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann
2022	A*	`conf`	Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann
2022	J	`jnl`	On the Robustness and Anomaly Detection of Sparse Neural Networks. CoRR Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann
2022	A*	`conf`	Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. NeurIPS Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis
2022	J	`jnl`	Predicting single-cell perturbation responses for unseen drugs. CoRR Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis
2022	Misc	`conf`	Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm. QCE Nicola Franco, Tom Wollschläger, Nicholas Gao, Jeanette Miriam Lorenz, Stephan Günnemann
2022	A*	`conf`	Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. NeurIPS Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
2022	J	`jnl`	Recursive SQL and GPU-support for in-database machine learning. Distributed Parallel Databases Maximilian E. Schüle, Harald Lang, Maximilian Springer, Alfons Kemper, Thomas Neumann, Stephan Günnemann
2022	J	`jnl`	Robustness verification of ReLU networks via quadratic programming. Mach. Learn. Aleksei Kuvshinov, Stephan Günnemann
2022	C	`conf`	Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning. ICMLA Felippe Schmoeller Roza, Hassan Rasheed, Karsten Roscher, Xiangyu Ning, Stephan Günnemann
2022	J	`jnl`	Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. CoRR Nicholas Gao, Stephan Günnemann
2022	B	`conf`	Supporting AI Engineering on the IoT Edge through Model-Driven TinyML. COMPSAC Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann
2022	J	`jnl`	Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation. Data Min. Knowl. Discov. Artur Mrowca, Florian Gyrock, Stephan Günnemann
2022	—	`conf`	Towards Model-Driven Engineering for Quantum AI. GI-Jahrestagung Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann
2022	—	`conf`	Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model. CVPR Workshops Codrut-Andrei Diaconu, Sudipan Saha, Stephan Günnemann, Xiao Xiang Zhu
2022	J	`jnl`	United States Politicians' Tone Became More Negative with 2016 Primary Campaigns. CoRR Jonathan Külz, Andreas Spitz, Ahmad Abu-Akel, Stephan Günnemann, Robert West
2022	J	`jnl`	Unveiling the Sampling Density in Non-Uniform Geometric Graphs. CoRR Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie
2022	A*	`conf`	Winning the Lottery Ahead of Time: Efficient Early Network Pruning. ICML John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann
2022	J	`jnl`	Winning the Lottery Ahead of Time: Efficient Early Network Pruning. CoRR John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann
2022	J	`jnl`	torchode: A Parallel ODE Solver for PyTorch. CoRR Marten Lienen, Stephan Günnemann
2021	J	`jnl`	3D Infomax improves GNNs for Molecular Property Prediction. CoRR Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò
2021	J	`jnl`	A Model-Driven Engineering Approach to Machine Learning and Software Modeling. CoRR Armin Moin, Atta Badii, Stephan Günnemann
2021	J	`jnl`	A Study of Joint Graph Inference and Forecasting. CoRR Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus
2021	J	`jnl`	Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. CoRR Nicholas Gao, Stephan Günnemann
2021	A*	`conf`	Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. ICLR Jan Schuchardt, Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann
2021	A	`conf`	Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions. AISTATS Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann
2021	A*	`conf`	Detecting Anomalous Event Sequences with Temporal Point Processes. NeurIPS Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
2021	J	`jnl`	Detecting Anomalous Event Sequences with Temporal Point Processes. CoRR Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
2021	A*	`conf`	Directional Message Passing on Molecular Graphs via Synthetic Coordinates. NeurIPS Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann
2021	J	`jnl`	Directional Message Passing on Molecular Graphs via Synthetic Coordinates. CoRR Johannes Klicpera, Chandan Yeshwanth, Stephan Günnemann
2021	—	`conf`	Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments. AISafety@IJCAI Tom Haider, Felippe Schmoeller Roza, Dirk Eilers, Karsten Roscher, Stephan Günnemann
2021	J	`jnl`	Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications. CoRR Armin Moin, Atta Badii, Stephan Günnemann
2021	A*	`conf`	Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann
2021	A*	`conf`	GemNet: Universal Directional Graph Neural Networks for Molecules. NeurIPS Johannes Gasteiger, Florian Becker, Stephan Günnemann
2021	J	`jnl`	GemNet: Universal Directional Graph Neural Networks for Molecules. CoRR Johannes Gasteiger, Florian Becker, Stephan Günnemann
2021	J	`jnl`	Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. CoRR Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
2021	A*	`conf`	Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. NeurIPS Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
2021	J	`jnl`	Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
2021	J	`jnl`	Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering. CoRR Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann
2021	Misc	`conf`	Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering. ISWC Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann
2021	B	`conf`	In-Database Machine Learning with SQL on GPUs. SSDBM Maximilian E. Schüle, Harald Lang, Maximilian Springer, Alfons Kemper, Thomas Neumann, Stephan Günnemann
2021	J	`jnl`	Intriguing Properties of Input-dependent Randomized Smoothing. CoRR Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann
2021	A*	`conf`	Language-Agnostic Representation Learning of Source Code from Structure and Context. ICLR Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann
2021	J	`jnl`	Language-Agnostic Representation Learning of Source Code from Structure and Context. CoRR Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann
2021	J	`jnl`	MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence. CoRR Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann
2021	J	`jnl`	ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. CoRR Armin Moin, Andrei Mituca, Atta Badii, Stephan Günnemann
2021	J	`jnl`	Mining communities and their descriptions on attributed graphs: a survey. Data Min. Knowl. Discov. Martin Atzmueller, Stephan Günnemann, Albrecht Zimmermann
2021	J	`jnl`	Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. CoRR Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann
2021	A*	`conf`	Neural Flows: Efficient Alternative to Neural ODEs. NeurIPS Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
2021	J	`jnl`	Neural Flows: Efficient Alternative to Neural ODEs. CoRR Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
2021	A*	`conf`	Neural Temporal Point Processes: A Review. IJCAI Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann
2021	J	`jnl`	Neural Temporal Point Processes: A Review. CoRR Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann
2021	A	`conf`	OODformer: Out-Of-Distribution Detection Transformer. BMVC Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp
2021	J	`jnl`	OODformer: Out-Of-Distribution Detection Transformer. CoRR Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp
2021	J	`jnl`	On Out-of-distribution Detection with Energy-based Models. CoRR Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2021	J	`jnl`	On Second-order Optimization Methods for Federated Learning. CoRR Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich
2021	J	`jnl`	Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training. Mach. Learn. Anna-Kathrin Kopetzki, Stephan Günnemann
2021	A*	`conf`	Robustness of Graph Neural Networks at Scale. NeurIPS Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann
2021	J	`jnl`	Robustness of Graph Neural Networks at Scale. CoRR Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann
2021	A*	`conf`	Scalable Normalizing Flows for Permutation Invariant Densities. ICML Marin Bilos, Stephan Günnemann
2021	A*	`conf`	Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. ICML Johannes Klicpera, Marten Lienen, Stephan Günnemann
2021	J	`jnl`	Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. CoRR Johannes Klicpera, Marten Lienen, Stephan Günnemann
2021	J	`jnl`	Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph). CoRR Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze
2021	—	`conf`	Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience. NeurIPS Datasets and Benchmarks Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Anjany Sekuboyina, Mihail I. Todorov, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze
2020	J	`jnl`	Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns. ACM Trans. Knowl. Discov. Data Daniel Zügner, Oliver Borchert, Amir Akbarnejad, Stephan Günnemann
2020	—	`conf`	Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection. ECCV Workshops (6) Felippe Schmoeller Roza, Maximilian Henne, Karsten Roscher, Stephan Günnemann
2020	A*	`conf`	Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. KDD Daniel Zügner, Stephan Günnemann
2020	A*	`conf`	Continual Learning with Bayesian Neural Networks for Non-Stationary Data. ICLR Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann
2020	A*	`conf`	Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
2020	J	`jnl`	Deep Representation Learning and Clustering of Traffic Scenarios. CoRR Nick Harmening, Marin Bilos, Stephan Günnemann
2020	A*	`conf`	Directional Message Passing for Molecular Graphs. ICLR Johannes Klicpera, Janek Groß, Stephan Günnemann
2020	J	`jnl`	Directional Message Passing for Molecular Graphs. CoRR Johannes Klicpera, Janek Groß, Stephan Günnemann
2020	A*	`conf`	Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. ICML Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann
2020	J	`jnl`	Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. CoRR Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann
2020	J	`jnl`	Equivariant Normalizing Flows for Point Processes and Sets. CoRR Marin Bilos, Stephan Günnemann
2020	J	`jnl`	Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? CoRR Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann
2020	A*	`conf`	Fast and Flexible Temporal Point Processes with Triangular Maps. NeurIPS Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann
2020	J	`jnl`	Fast and Flexible Temporal Point Processes with Triangular Maps. CoRR Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann
2020	J	`jnl`	Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. CoRR Johannes Klicpera, Shankari Giri, Johannes T. Margraf, Stephan Günnemann
2020	J	`jnl`	From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2). CoRR Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann
2020	—	`conf`	From things' modeling language (ThingML) to things' machine learning (ThingML2). MoDELS (Companion) Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann
2020	—	`conf`	Gauss Shift: Density Attractor Clustering Faster Than Mean Shift. ECML/PKDD (1) Richard Leibrandt, Stephan Günnemann
2020	J	`jnl`	Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs. CoRR Zhen Han, Yuyi Wang, Yunpu Ma, Stephan Günnemann, Volker Tresp
2020	—	`conf`	Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs. AKBC Zhen Han, Yunpu Ma, Yuyi Wang, Stephan Günnemann, Volker Tresp
2020	A	`conf`	Group Centrality Maximization for Large-scale Graphs. ALENEX Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke
2020	A*	`conf`	Intensity-Free Learning of Temporal Point Processes. ICLR Oleksandr Shchur, Marin Bilos, Stephan Günnemann
2020	A*	`conf`	Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. NeurIPS Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2020	J	`jnl`	Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. CoRR Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2020	J	`jnl`	Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training. CoRR Anna-Kathrin Kopetzki, Stephan Günnemann
2020	A*	`conf`	Reliable Graph Neural Networks via Robust Aggregation. NeurIPS Simon Geisler, Daniel Zügner, Stephan Günnemann
2020	J	`jnl`	Reliable Graph Neural Networks via Robust Aggregation. CoRR Simon Geisler, Daniel Zügner, Stephan Günnemann
2020	A*	`conf`	Scaling Graph Neural Networks with Approximate PageRank. KDD Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann
2020	J	`jnl`	Scaling Graph Neural Networks with Approximate PageRank. CoRR Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann
2020	J	`jnl`	Scene Graph Reasoning for Visual Question Answering. CoRR Marcel Hildebrandt, Hang Li, Rajat Koner, Volker Tresp, Stephan Günnemann
2020	J	`jnl`	ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning. CoRR Armin Moin, Stephan Rössler, Stephan Günnemann
2019	—	`conf`	Adversarial Attacks on Graph Neural Networks via Meta Learning. ICLR (Poster) Daniel Zügner, Stephan Günnemann
2019	J	`jnl`	Adversarial Attacks on Graph Neural Networks via Meta Learning. CoRR Daniel Zügner, Stephan Günnemann
2019	—	`conf`	Adversarial Attacks on Graph Neural Networks. GI-Jahrestagung Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
2019	A*	`conf`	Adversarial Attacks on Neural Networks for Graph Data. IJCAI Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
2019	A*	`conf`	Adversarial Attacks on Node Embeddings via Graph Poisoning. ICML Aleksandar Bojchevski, Stephan Günnemann
2019	A*	`conf`	Certifiable Robustness and Robust Training for Graph Convolutional Networks. KDD Daniel Zügner, Stephan Günnemann
2019	J	`jnl`	Certifiable Robustness and Robust Training for Graph Convolutional Networks. CoRR Daniel Zügner, Stephan Günnemann
2019	A*	`conf`	Certifiable Robustness to Graph Perturbations. NeurIPS Aleksandar Bojchevski, Stephan Günnemann
2019	J	`jnl`	Certifiable Robustness to Graph Perturbations. CoRR Aleksandar Bojchevski, Stephan Günnemann
2019	A*	`conf`	Diffusion Improves Graph Learning. NeurIPS Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann
2019	J	`jnl`	Diffusion Improves Graph Learning. CoRR Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann
2019	A*	`conf`	Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. NeurIPS Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
2019	A*	`conf`	GhostLink: Latent Network Inference for Influence-aware Recommendation. WWW Subhabrata Mukherjee, Stephan Günnemann
2019	J	`jnl`	GhostLink: Latent Network Inference for Influence-aware Recommendation. CoRR Subhabrata Mukherjee, Stephan Günnemann
2019	J	`jnl`	Group Centrality Maximization for Large-scale Graphs. CoRR Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke
2019	Misc	`conf`	In-Database Machine Learning: Gradient Descent and Tensor Algebra for Main Memory Database Systems. BTW Maximilian E. Schüle, Frédéric Simonis, Thomas Heyenbrock, Alfons Kemper, Stephan Günnemann, Thomas Neumann
2019	J	`jnl`	Intensity-Free Learning of Temporal Point Processes. CoRR Oleksandr Shchur, Marin Bilos, Stephan Günnemann
2019	A*	`conf`	Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference. DAC Artur Mrowca, Martin Nocker, Sebastian Steinhorst, Stephan Günnemann
2019	B	`conf`	ML2SQL - Compiling a Declarative Machine Learning Language to SQL and Python. EDBT Maximilian E. Schüle, Matthias Bungeroth, Dimitri Vorona, Alfons Kemper, Stephan Günnemann, Thomas Neumann
2019	—	`conf`	MLearn: A Declarative Machine Learning Language for Database Systems. DEEM@SIGMOD Maximilian E. Schüle, Matthias Bungeroth, Alfons Kemper, Stephan Günnemann, Thomas Neumann
2019	A*	`conf`	Multi-Source Neural Variational Inference. AAAI Richard Kurle, Stephan Günnemann, Patrick van der Smagt
2019	J	`jnl`	Oktoberfest Food Dataset. CoRR Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann
2019	J	`jnl`	Overlapping Community Detection with Graph Neural Networks. CoRR Oleksandr Shchur, Stephan Günnemann
2019	—	`conf`	Predict then Propagate: Graph Neural Networks meet Personalized PageRank. ICLR (Poster) Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann
2019	J	`jnl`	Stability and dynamics of communities on online question-answer sites. Soc. Networks Saskia Metzler, Stephan Günnemann, Pauli Miettinen
2019	B	`conf`	The Power of SQL Lambda Functions. EDBT Maximilian E. Schüle, Dimitri Vorona, Linnea Passing, Harald Lang, Alfons Kemper, Stephan Günnemann, Thomas Neumann
2019	A*	`conf`	Uncertainty on Asynchronous Time Event Prediction. NeurIPS Bertrand Charpentier, Marin Bilos, Stephan Günnemann
2019	J	`jnl`	Uncertainty on Asynchronous Time Event Prediction. CoRR Marin Bilos, Bertrand Charpentier, Stephan Günnemann
2018	A*	`conf`	Adversarial Attacks on Neural Networks for Graph Data. KDD Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
2018	J	`jnl`	Adversarial Attacks on Neural Networks for Graph Data. CoRR Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
2018	J	`jnl`	Adversarial Attacks on Node Embeddings. CoRR Aleksandar Bojchevski, Stephan Günnemann
2018	A	`conf`	An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors. SDM Marawan Shalaby, Jan Stutzki, Matthias Schubert, Stephan Günnemann
2018	—	`conf`	Anomaly Detection in Car-Booking Graphs. ICDM Workshops Oleksandr Shchur, Aleksandar Bojchevski, Mohamed Farghal, Stephan Günnemann, Yusuf Saber
2018	A*	`conf`	Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. AAAI Aleksandar Bojchevski, Stephan Günnemann
2018	—	`conf`	Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. ICLR (Poster) Aleksandar Bojchevski, Stephan Günnemann
2018	—	`conf`	Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping. ECML/PKDD (3) Artur Mrowca, Barbara Moser, Stephan Günnemann
2018	J	`jnl`	Dual-Primal Graph Convolutional Networks. CoRR Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein
2018	J	`jnl`	Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. CoRR Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
2018	B	`conf`	Intrinsic Degree: An Estimator of the Local Growth Rate in Graphs. SISAP Lorenzo von Ritter, Michael E. Houle, Stephan Günnemann
2018	A	`conf`	Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation. SDM Richard Leibrandt, Stephan Günnemann
2018	J	`jnl`	Mining Contrasting Quasi-Clique Patterns. CoRR Roberto Alonso, Stephan Günnemann
2018	J	`jnl`	Multi-Source Neural Variational Inference. CoRR Richard Kurle, Stephan Günnemann, Patrick van der Smagt
2018	A*	`conf`	NetGAN: Generating Graphs via Random Walks. ICML Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann
2018	J	`jnl`	NetGAN: Generating Graphs via Random Walks. CoRR Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann
2018	J	`jnl`	Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank. CoRR Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann
2018	J	`jnl`	Pitfalls of Graph Neural Network Evaluation. CoRR Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann
2018	—	`conf`	Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics. ITSC Peter Wolf, Artur Mrowca, Tam Thanh Nguyen, Bernard Bäker, Stephan Günnemann
2018	—	`conf`	ThingML+: Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning. MoDELS (Workshops) Armin Moin, Stephan Rössler, Stephan Günnemann
2017	J	`jnl`	Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs. Proc. VLDB Endow. Manuel Then, Timo Kersten, Stephan Günnemann, Alfons Kemper, Thomas Neumann
2017	J	`jnl`	Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking. CoRR Aleksandar Bojchevski, Stephan Günnemann
2017	B	`conf`	Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data. SSTD Nina C. Hubig, Philip Fengler, Andreas Züfle, Ruixin Yang, Stephan Günnemann
2017	Misc	`conf`	Efficient Batched Distance and Centrality Computation in Unweighted and Weighted Graphs. BTW Manuel Then, Stephan Günnemann, Alfons Kemper, Thomas Neumann
2017	J	`jnl`	Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs. Datenbank-Spektrum Manuel Then, Stephan Günnemann, Alfons Kemper, Thomas Neumann
2017	J	`jnl`	Introduction to Tensor Decompositions and their Applications in Machine Learning. CoRR Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann
2017	J	`jnl`	Machine Learning Meets Databases. Datenbank-Spektrum Stephan Günnemann
2017	J	`jnl`	MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowl. Inf. Syst. Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl
2017	J	`jnl`	Personalized Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion. CoRR Subhabrata Mukherjee, Stephan Günnemann, Gerhard Weikum
2017	A*	`conf`	Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings. KDD Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann
2017	B	`conf`	SQL- and Operator-centric Data Analytics in Relational Main-Memory Databases. EDBT Linnea Passing, Manuel Then, Nina C. Hubig, Harald Lang, Michael Schreier, Stephan Günnemann, Alfons Kemper, Thomas Neumann
2017	A	`conf`	The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation. SDM Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos
2017	J	`jnl`	ZooBP: Belief Propagation for Heterogeneous Networks. Proc. VLDB Endow. Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos, Disha Makhija, Mohit Kumar
2016	A	`conf`	BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. SDM Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
2016	A*	`conf`	Continuous Experience-aware Language Model. KDD Subhabrata Mukherjee, Stephan Günnemann, Gerhard Weikum
2016	J	`jnl`	Discovery of "comet" communities in temporal and labeled graphs Com Knowl. Inf. Syst. Miguel Araujo, Stephan Günnemann, Spiros Papadimitriou, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra
2016	—	`conf`	EdgeCentric: Anomaly Detection in Edge-Attributed Networks. ICDM Workshops Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos
2016	J	`jnl`	Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques. CoRR Saskia Metzler, Stephan Günnemann, Pauli Miettinen
2016	A*	`conf`	Hyperbolae are No Hyperbole: Modelling Communities That are Not Cliques. ICDM Saskia Metzler, Stephan Günnemann, Pauli Miettinen
2015	A*	`conf`	Automatic Taxonomy Extraction from Bipartite Graphs. ICDM Tobias Kötter, Stephan Günnemann, Michael R. Berthold, Christos Faloutsos
2015	J	`jnl`	BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. CoRR Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
2015	J	`jnl`	EdgeCentric: Anomaly Detection in Edge-Attributed Networks. CoRR Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos
2015	—	`conf`	Effiziente Integration von Data- und Graph-Mining-Algorithmen in relationale Datenbanksysteme. LWA Manuel Then, Linnea Passing, Nina C. Hubig, Stephan Günnemann, Alfons Kemper, Thomas Neumann
2015	—	`conf`	Extracting Taxonomies from Bipartite Graphs. WWW (Companion Volume) Tobias Kötter, Stephan Günnemann, Christos Faloutsos, Michael R. Berthold
2015	J	`jnl`	Linearized and Single-Pass Belief Propagation. Proc. VLDB Endow. Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, Christos Faloutsos
2015	J	`jnl`	MultiClust special issue on discovering, summarizing and using multiple clusterings. Mach. Learn. Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl, Jennifer G. Dy
2015	A	`conf`	Preferential Attachment in Graphs with Affinities. AISTATS Jay Lee, Manzil Zaheer, Stephan Günnemann, Alexander J. Smola
2014	—	`conf`	Beyond Blocks: Hyperbolic Community Detection. ECML/PKDD (1) Miguel Araujo, Stephan Günnemann, Gonzalo Mateos, Christos Faloutsos
2014	—	`conf`	Com2: Fast Automatic Discovery of Temporal ('Comet') Communities. PAKDD (2) Miguel Araujo, Spiros Papadimitriou, Stephan Günnemann, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra
2014	A*	`conf`	Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution. KDD Stephan Günnemann, Nikou Günnemann, Christos Faloutsos
2014	—	`conf`	Fault-Tolerant Concept Detection in Information Networks. PAKDD (1) Tobias Kötter, Stephan Günnemann, Michael R. Berthold, Christos Faloutsos
2014	J	`jnl`	GAMer: a synthesis of subspace clustering and dense subgraph mining. Knowl. Inf. Syst. Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl
2014	J	`jnl`	KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks. CoRR Stephan Günnemann, Hardy Kremer, Matthias Hannen, Thomas Seidl
2014	J	`jnl`	Linearized and Turbo Belief Propagation. CoRR Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, Christos Faloutsos
2014	A*	`conf`	Robust multivariate autoregression for anomaly detection in dynamic product ratings. WWW Nikou Günnemann, Stephan Günnemann, Christos Faloutsos
2014	A*	`conf`	SMVC: semi-supervised multi-view clustering in subspace projections. KDD Stephan Günnemann, Ines Färber, Matthias Sebastian Rüdiger, Thomas Seidl
2013	—	`conf`	An Evaluation Framework for Temporal Subspace Clustering Approaches. ICDM Workshops Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl
2013	—	`conf`	Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors. PAKDD (1) Stephan Günnemann, Brigitte Boden, Ines Färber, Thomas Seidl
2013	—	`conf`	Finding contexts of social influence in online social networks. SNAKDD Jennifer H. Nguyen, Bo Hu, Stephan Günnemann, Martin Ester
2013	A*	`conf`	Mixed Membership Subspace Clustering. ICDM Stephan Günnemann, Christos Faloutsos
2013	B	`conf`	Nesting the earth mover's distance for effective cluster tracing. SSDBM Hardy Kremer, Stephan Günnemann, Simon Wollwage, Thomas Seidl
2013	B	`conf`	RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs. SSDBM Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl
2013	A*	`conf`	Spectral Subspace Clustering for Graphs with Feature Vectors. ICDM Stephan Günnemann, Ines Färber, Sebastian Raubach, Thomas Seidl
2013	—	`conf`	Stochastic subspace search for top-k multi-view clustering. MultiClust@KDD Geng Li, Stephan Günnemann, Mohammed J. Zaki
2013	Misc	`conf`	Subspace Clustering for Complex Data. BTW Stephan Günnemann
2012	—	`conf`	A Subspace Clustering Extension for the KNIME Data Mining Framework. ICDM Workshops Stephan Günnemann, Hardy Kremer, Richard Musiol, Roman Haag, Thomas Seidl
2012	A*	`conf`	Assessing the Significance of Data Mining Results on Graphs with Feature Vectors. ICDM Stephan Günnemann, Phuong Dao, Mohsen Jamali, Martin Ester
2012	A*	`conf`	Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDE Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl
2012	A*	`conf`	Effective and Robust Mining of Temporal Subspace Clusters. ICDM Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl
2012	J	`jnl`	Finding density-based subspace clusters in graphs with feature vectors. Data Min. Knowl. Discov. Stephan Günnemann, Brigitte Boden, Thomas Seidl
2012	A*	`conf`	Mining coherent subgraphs in multi-layer graphs with edge labels. KDD Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl
2012	—	`conf`	Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases. PAKDD (1) Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl
2012	A*	`conf`	Multi-view clustering using mixture models in subspace projections. KDD Stephan Günnemann, Ines Färber, Thomas Seidl
2012	—	`—`	Subspace clustering for complex data. Stephan Günnemann
2012	A*	`conf`	Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data. KDD Stephan Günnemann, Ines Färber, Kittipat Virochsiri, Thomas Seidl
2012	B	`conf`	Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types. SSDBM Stephan Günnemann, Brigitte Boden, Thomas Seidl
2012	J	`jnl`	Tracing Evolving Subspace Clusters in Temporal Climate Data. Data Min. Knowl. Discov. Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl
2012	A	`conf`	Tracing clusters in evolving graphs with node attributes. CIKM Brigitte Boden, Stephan Günnemann, Thomas Seidl
2011	Misc	`conf`	A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. BTW Emmanuel Müller, Ira Assent, Stephan Günnemann, Patrick Gerwert, Matthias Hannen, Timm Jansen, Thomas Seidl
2011	—	`conf`	DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors. ECML/PKDD (1) Stephan Günnemann, Brigitte Boden, Thomas Seidl
2011	B	`conf`	Efficient Processing of Multiple DTW Queries in Time Series Databases. SSDBM Hardy Kremer, Stephan Günnemann, Anca Maria Ivanescu, Ira Assent, Thomas Seidl
2011	A	`conf`	External evaluation measures for subspace clustering. CIKM Stephan Günnemann, Ines Färber, Emmanuel Müller, Ira Assent, Thomas Seidl
2011	—	`conf`	Finding Density-Based Subspace Clusters in Graphs with Feature Vectors. LWA Stephan Günnemann, Brigitte Boden, Thomas Seidl
2011	A*	`conf`	Flexible Fault Tolerant Subspace Clustering for Data with Missing Values. ICDM Stephan Günnemann, Emmanuel Müller, Sebastian Raubach, Thomas Seidl
2011	—	`ed.`	Proceedings of the 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, Athens, Greece, September 5, 2011, in conjunction with ECML/PKDD 2011 MultiClust@ECML/PKDD Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl
2011	A	`conf`	Scalable density-based subspace clustering. CIKM Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl
2011	B	`conf`	Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations. EDBT Stephan Günnemann, Hardy Kremer, Dominik Lenhard, Thomas Seidl
2011	—	`conf`	Tracing Evolving Clusters by Subspace and Value Similarity. PAKDD (2) Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl
2010	J	`jnl`	CoDA: Interactive Cluster Based Concept Discovery. Proc. VLDB Endow. Stephan Günnemann, Ines Färber, Hardy Kremer, Thomas Seidl
2010	—	`conf`	Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques. ICDM Workshops Hardy Kremer, Stephan Günnemann, Thomas Seidl
2010	A*	`conf`	Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDM Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl
2010	B	`conf`	MC-Tree: Improving Bayesian Anytime Classification. SSDBM Philipp Kranen, Stephan Günnemann, Sergej Fries, Thomas Seidl
2010	—	`conf`	MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions. ICDM Workshops Stephan Günnemann, Hardy Kremer, Ines Färber, Thomas Seidl
2010	B	`conf`	Pattern detector: fast detection of suspicious stream patterns for immediate reaction. EDBT Ira Assent, Hardy Kremer, Stephan Günnemann, Thomas Seidl
2010	—	`conf`	Subgraph Mining on Directed and Weighted Graphs. PAKDD (2) Stephan Günnemann, Thomas Seidl
2010	A*	`conf`	Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. ICDM Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl
2010	A	`conf`	Subspace Clustering for Uncertain Data. SDM Stephan Günnemann, Hardy Kremer, Thomas Seidl
2009	A	`conf`	DensEst: Density Estimation for Data Mining in High Dimensional Spaces. SDM Emmanuel Müller, Ira Assent, Ralph Krieger, Stephan Günnemann, Thomas Seidl
2009	A	`conf`	Detection of orthogonal concepts in subspaces of high dimensional data. CIKM Stephan Günnemann, Emmanuel Müller, Ines Färber, Thomas Seidl
2009	J	`jnl`	Evaluating Clustering in Subspace Projections of High Dimensional Data. Proc. VLDB Endow. Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl
2009	Misc	`conf`	High-Dimensional Indexing for Multimedia Features. BTW Ira Assent, Stephan Günnemann, Hardy Kremer, Thomas Seidl
2009	A*	`conf`	Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data. ICDM Emmanuel Müller, Ira Assent, Stephan Günnemann, Ralph Krieger, Thomas Seidl