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ORCIDStephan Günnemann
Person information
- affiliation: Technical University of Munich, Germany
- affiliation (former): Carnegie Mellon University, Pittsburgh, USA
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Books and Theses
- 2012
[b1]Stephan Günnemann:
Subspace clustering for complex data. RWTH Aachen University, 2012
Journal Articles
- 2023
- [j26]Richard Leibrandt
, Stephan Günnemann:
Generalized density attractor clustering for incomplete data. Data Min. Knowl. Discov. 37(2): 970-1009 (2023) - [j25]Hao Lin, Hongfu Liu, Junjie Wu, Hong Li, Stephan Günnemann:
Algorithm 1038: KCC: A MATLAB Package for k-Means-based Consensus Clustering. ACM Trans. Math. Softw. 49(4): 40:1-40:27 (2023) - 2022
- [j24]Artur Mrowca, Florian Gyrock, Stephan Günnemann:
Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation. Data Min. Knowl. Discov. 36(1): 240-294 (2022) - [j23]Maximilian E. Schüle, Harald Lang, Maximilian Springer, Alfons Kemper, Thomas Neumann, Stephan Günnemann:
Recursive SQL and GPU-support for in-database machine learning. Distributed Parallel Databases 40(2-3): 205-259 (2022) - [j22]Aleksei Kuvshinov, Stephan Günnemann:
Robustness verification of ReLU networks via quadratic programming. Mach. Learn. 111(7): 2407-2433 (2022) - [j21]Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, Johannes T. Margraf:
How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? Mach. Learn. Sci. Technol. 3(4): 45010 (2022) - [j20]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
A model-driven approach to machine learning and software modeling for the IoT. Softw. Syst. Model. 21(3): 987-1014 (2022) - [j19]Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary W. Ulissi, C. Lawrence Zitnick, Abhishek Das:
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets. Trans. Mach. Learn. Res. 2022 (2022) - [j18]Kevin Kennard Thiel, Florian Naumann, Eduard Jundt, Stephan Günnemann, Gudrun Klinker:
C.DOT - Convolutional Deep Object Tracker for Augmented Reality Based Purely on Synthetic Data. IEEE Trans. Vis. Comput. Graph. 28(12): 4434-4451 (2022) - 2021
- [j17]Martin Atzmueller, Stephan Günnemann, Albrecht Zimmermann:
Mining communities and their descriptions on attributed graphs: a survey. Data Min. Knowl. Discov. 35(3): 661-687 (2021) - [j16]Anna-Kathrin Kopetzki, Stephan Günnemann:
Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training. Mach. Learn. 110(6): 1175-1197 (2021) - 2020
- [j15]Daniel Zügner, Oliver Borchert, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns. ACM Trans. Knowl. Discov. Data 14(5): 57:1-57:31 (2020) - 2019
- [j14]Saskia Metzler, Stephan Günnemann, Pauli Miettinen:
Stability and dynamics of communities on online question-answer sites. Soc. Networks 58: 50-58 (2019) - 2017
- [j13]Stephan Günnemann:
Machine Learning Meets Databases. Datenbank-Spektrum 17(1): 77-83 (2017) - [j12]Manuel Then, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs. Datenbank-Spektrum 17(2): 169-182 (2017) - [j11]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowl. Inf. Syst. 50(2): 417-446 (2017) - [j10]Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos, Disha Makhija, Mohit Kumar:
ZooBP: Belief Propagation for Heterogeneous Networks. Proc. VLDB Endow. 10(5): 625-636 (2017) - [j9]Manuel Then, Timo Kersten, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs. Proc. VLDB Endow. 10(8): 877-888 (2017) - 2016
- [j8]Miguel Araujo, Stephan Günnemann, Spiros Papadimitriou, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra:
Discovery of "comet" communities in temporal and labeled graphs Com^2. Knowl. Inf. Syst. 46(3): 657-677 (2016) - 2015
- [j7]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl, Jennifer G. Dy:
MultiClust special issue on discovering, summarizing and using multiple clusterings. Mach. Learn. 98(1-2): 1-5 (2015) - [j6]Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, Christos Faloutsos:
Linearized and Single-Pass Belief Propagation. Proc. VLDB Endow. 8(5): 581-592 (2015) - 2014
- [j5]Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl:
GAMer: a synthesis of subspace clustering and dense subgraph mining. Knowl. Inf. Syst. 40(2): 243-278 (2014) - 2012
- [j4]Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl:
Tracing Evolving Subspace Clusters in Temporal Climate Data. Data Min. Knowl. Discov. 24(2): 387-410 (2012) - [j3]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
Finding density-based subspace clusters in graphs with feature vectors. Data Min. Knowl. Discov. 25(2): 243-269 (2012) - 2010
- [j2]Stephan Günnemann, Ines Färber, Hardy Kremer, Thomas Seidl:
CoDA: Interactive Cluster Based Concept Discovery. Proc. VLDB Endow. 3(2): 1633-1636 (2010) - 2009
- [j1]Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl:
Evaluating Clustering in Subspace Projections of High Dimensional Data. Proc. VLDB Endow. 2(1): 1270-1281 (2009)
Conference and Workshop Papers
- 2023
- [c165]Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann:
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out Of Distribution. AISafety/SafeRL@IJCAI 2023 - [c164]Tom Haider, Karsten Roscher, Felippe Schmoeller da Roza, Stephan Günnemann:
Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models. AAMAS 2023: 851-859 - [c163]Sebastian Schmidt, Stephan Günnemann:
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. BMVC 2023: 664 - [c162]Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
Enabling Machine Learning in Software Architecture Frameworks. CAIN 2023: 92-93 - [c161]Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel:
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRL 2023: 3214-3241 - [c160]Nicholas Gao, Stephan Günnemann:
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. ICLR 2023 - [c159]Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann:
Revisiting Robustness in Graph Machine Learning. ICLR 2023 - [c158]Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie:
Unveiling the sampling density in non-uniform geometric graphs. ICLR 2023 - [c157]Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann:
Localized Randomized Smoothing for Collective Robustness Certification. ICLR 2023 - [c156]Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. ICML 2023: 2452-2470 - [c155]Nicholas Gao, Stephan Günnemann:
Generalizing Neural Wave Functions. ICML 2023: 10708-10726 - [c154]Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. ICML 2023: 11144-11172 - [c153]Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. ICML 2023: 17544-17563 - [c152]Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. ICML 2023: 37133-37156 - [c151]Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann:
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. IV 2023: 1-8 - [c150]Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. NeurIPS 2023 - [c149]David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann:
Add and Thin: Diffusion for Temporal Point Processes. NeurIPS 2023 - [c148]Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Hierarchical Randomized Smoothing. NeurIPS 2023 - [c147]Jan Schuchardt, Yan Scholten, Stephan Günnemann:
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. NeurIPS 2023 - [c146]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. ECML/PKDD (1) 2023: 459-474 - [c145]Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz:
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. QCE 2023: 524-534 - 2022
- [c144]Aleksei Kuvshinov, Daniel Knobloch, Daniel Külzer, Elen Vardanyan, Stephan Günnemann:
Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks. AAAI 2022: 12552-12558 - [c143]Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher, Stephan Günnemann:
Is it all a cluster game? - Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space. SafeAI@AAAI 2022 - [c142]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
Supporting AI Engineering on the IoT Edge through Model-Driven TinyML. COMPSAC 2022: 884-893 - [c141]Codrut-Andrei Diaconu, Sudipan Saha, Stephan Günnemann, Xiao Xiang Zhu:
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model. CVPR Workshops 2022: 1361-1370 - [c140]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
Towards Model-Driven Engineering for Quantum AI. GI-Jahrestagung 2022: 1121-1131 - [c139]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR 2022 - [c138]Bertrand Charpentier, Simon Kibler, Stephan Günnemann:
Differentiable DAG Sampling. ICLR 2022 - [c137]Nicholas Gao, Stephan Günnemann:
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. ICLR 2022 - [c136]Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. ICLR 2022 - [c135]Marten Lienen, Stephan Günnemann:
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. ICLR 2022 - [c134]Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann:
End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR 2022 - [c133]John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann:
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. ICML 2022: 18293-18309 - [c132]Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió:
3D Infomax improves GNNs for Molecular Property Prediction. ICML 2022: 20479-20502 - [c131]Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann:
Intriguing Properties of Input-Dependent Randomized Smoothing. ICML 2022: 20697-20743 - [c130]Felippe Schmoeller Roza, Hassan Rasheed, Karsten Roscher, Xiangyu Ning, Stephan Günnemann:
Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning. ICMLA 2022: 737-742 - [c129]Armin Moin, Andrei Mituca, Moharram Challenger, Atta Badii, Stephan Günnemann:
ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. ICSE-Companion 2022: 144-148 - [c128]Johannes Gasteiger, Chendi Qian, Stephan Günnemann:
Influence-Based Mini-Batching for Graph Neural Networks. LoG 2022: 9 - [c127]Alexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie:
A Systematic Evaluation of Node Embedding Robustness. LoG 2022: 42 - [c126]Jörg Christian Kirchhof, Evgeny Kusmenko, Jonas Ritz, Bernhard Rumpe, Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
MDE for machine learning-enabled software systems: a case study and comparison of MontiAnna & ML-Quadrat. MoDELS (Companion) 2022: 380-387 - [c125]Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis:
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. NeurIPS 2022 - [c124]Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski:
Are Defenses for Graph Neural Networks Robust? NeurIPS 2022 - [c123]Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann:
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. NeurIPS 2022 - [c122]Jan Schuchardt, Stephan Günnemann:
Invariance-Aware Randomized Smoothing Certificates. NeurIPS 2022 - [c121]Nicola Franco, Tom Wollschläger, Nicholas Gao, Jeanette Miriam Lorenz, Stephan Günnemann:
Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm. QCE 2022: 142-153 - 2021
- [c120]Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann:
Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions. AISTATS 2021: 3763-3771 - [c119]Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp:
OODformer: Out-Of-Distribution Detection Transformer. BMVC 2021: 209 - [c118]Jan Schuchardt, Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. ICLR 2021 - [c117]Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann:
Language-Agnostic Representation Learning of Source Code from Structure and Context. ICLR 2021 - [c116]Marin Bilos, Stephan Günnemann:
Scalable Normalizing Flows for Permutation Invariant Densities. ICML 2021: 957-967 - [c115]Johannes Klicpera, Marten Lienen, Stephan Günnemann:
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. ICML 2021: 5616-5627 - [c114]Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann:
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML 2021: 5707-5718 - [c113]Tom Haider, Felippe Schmoeller Roza, Dirk Eilers, Karsten Roscher, Stephan Günnemann:
Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments. AISafety@IJCAI 2021 - [c112]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann:
Neural Temporal Point Processes: A Review. IJCAI 2021: 4585-4593 - [c111]Johannes Gasteiger, Florian Becker, Stephan Günnemann:
GemNet: Universal Directional Graph Neural Networks for Molecules. NeurIPS 2021: 6790-6802 - [c110]Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann:
Robustness of Graph Neural Networks at Scale. NeurIPS 2021: 7637-7649 - [c109]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann:
Detecting Anomalous Event Sequences with Temporal Point Processes. NeurIPS 2021: 13419-13431 - [c108]Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann:
Directional Message Passing on Molecular Graphs via Synthetic Coordinates. NeurIPS 2021: 15421-15433 - [c107]Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann:
Neural Flows: Efficient Alternative to Neural ODEs. NeurIPS 2021: 21325-21337 - [c106]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:
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience. NeurIPS Datasets and Benchmarks 2021 - [c105]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. NeurIPS 2021: 18033-18048 - [c104]Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann:
Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering. ISWC 2021: 111-127 - [c103]Maximilian E. Schüle, Harald Lang, Maximilian Springer, Alfons Kemper, Thomas Neumann, Stephan Günnemann:
In-Database Machine Learning with SQL on GPUs. SSDBM 2021: 25-36 - 2020
- [c102]Zhen Han, Yunpu Ma, Yuyi Wang, Stephan Günnemann, Volker Tresp:
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs. AKBC 2020 - [c101]Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke:
Group Centrality Maximization for Large-scale Graphs. ALENEX 2020: 56-69 - [c100]Felippe Schmoeller Roza, Maximilian Henne, Karsten Roscher, Stephan Günnemann:
Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection. ECCV Workshops (6) 2020: 3-10 - [c99]Johannes Klicpera, Janek Groß, Stephan Günnemann:
Directional Message Passing for Molecular Graphs. ICLR 2020 - [c98]Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann:
Continual Learning with Bayesian Neural Networks for Non-Stationary Data. ICLR 2020 - [c97]Oleksandr Shchur, Marin Bilos, Stephan Günnemann:
Intensity-Free Learning of Temporal Point Processes. ICLR 2020 - [c96]Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. ICML 2020: 1003-1013 - [c95]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. KDD 2020: 1656-1665 - [c94]Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann:
Scaling Graph Neural Networks with Approximate PageRank. KDD 2020: 2464-2473 - [c93]Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann:
From things' modeling language (ThingML) to things' machine learning (ThingML2). MoDELS (Companion) 2020: 19:1-19:2 - [c92]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. NeurIPS 2020 - [c91]Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. NeurIPS 2020 - [c90]Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus:
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS 2020 - [c89]Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann:
Fast and Flexible Temporal Point Processes with Triangular Maps. NeurIPS 2020 - [c88]Richard Leibrandt, Stephan Günnemann:
Gauss Shift: Density Attractor Clustering Faster Than Mean Shift. ECML/PKDD (1) 2020: 125-142 - 2019
- [c87]Richard Kurle, Stephan Günnemann, Patrick van der Smagt:
Multi-Source Neural Variational Inference. AAAI 2019: 4114-4121 - [c86]Maximilian E. Schüle, Frédéric Simonis, Thomas Heyenbrock, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
In-Database Machine Learning: Gradient Descent and Tensor Algebra for Main Memory Database Systems. BTW 2019: 247-266 - [c85]Artur Mrowca, Martin Nocker, Sebastian Steinhorst, Stephan Günnemann:
Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference. DAC 2019: 96 - [c84]Maximilian E. Schüle, Dimitri Vorona, Linnea Passing, Harald Lang, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
The Power of SQL Lambda Functions. EDBT 2019: 534-537 - [c83]Maximilian E. Schüle, Matthias Bungeroth, Dimitri Vorona, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
ML2SQL - Compiling a Declarative Machine Learning Language to SQL and Python. EDBT 2019: 562-565 - [c82]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks. GI-Jahrestagung 2019: 251-252 - [c81]Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann:
Predict then Propagate: Graph Neural Networks meet Personalized PageRank. ICLR (Poster) 2019 - [c80]Daniel Zügner, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks via Meta Learning. ICLR (Poster) 2019 - [c79]Aleksandar Bojchevski, Stephan Günnemann:
Adversarial Attacks on Node Embeddings via Graph Poisoning. ICML 2019: 695-704 - [c78]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. IJCAI 2019: 6246-6250 - [c77]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness and Robust Training for Graph Convolutional Networks. KDD 2019: 246-256 - [c76]Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. NeurIPS 2019: 1394-1406 - [c75]Aleksandar Bojchevski, Stephan Günnemann:
Certifiable Robustness to Graph Perturbations. NeurIPS 2019: 8317-8328 - [c74]Bertrand Charpentier, Marin Bilos, Stephan Günnemann:
Uncertainty on Asynchronous Time Event Prediction. NeurIPS 2019: 12831-12840 - [c73]Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann:
Diffusion Improves Graph Learning. NeurIPS 2019: 13333-13345 - [c72]Maximilian E. Schüle, Matthias Bungeroth, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
MLearn: A Declarative Machine Learning Language for Database Systems. DEEM@SIGMOD 2019: 7:1-7:4 - [c71]Subhabrata Mukherjee, Stephan Günnemann:
GhostLink: Latent Network Inference for Influence-aware Recommendation. WWW 2019: 1310-1320 - 2018
- [c70]Aleksandar Bojchevski, Stephan Günnemann:
Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. AAAI 2018: 2738-2745 - [c69]Oleksandr Shchur, Aleksandar Bojchevski, Mohamed Farghal, Stephan Günnemann, Yusuf Saber:
Anomaly Detection in Car-Booking Graphs. ICDM Workshops 2018: 604-607 - [c68]Aleksandar Bojchevski, Stephan Günnemann:
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. ICLR (Poster) 2018 - [c67]Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann:
NetGAN: Generating Graphs via Random Walks. ICML 2018: 609-618 - [c66]Peter Wolf, Artur Mrowca, Tam Thanh Nguyen, Bernard Bäker, Stephan Günnemann:
Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics. ITSC 2018: 176-183 - [c65]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. KDD 2018: 2847-2856 - [c64]Armin Moin, Stephan Rössler, Stephan Günnemann:
ThingML+: Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning. MoDELS (Workshops) 2018: 521-523 - [c63]Artur Mrowca, Barbara Moser, Stephan Günnemann:
Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping. ECML/PKDD (3) 2018: 86-102 - [c62]Marawan Shalaby, Jan Stutzki, Matthias Schubert, Stephan Günnemann:
An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors. SDM 2018: 495-503 - [c61]Richard Leibrandt, Stephan Günnemann:
Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation. SDM 2018: 747-755 - [c60]Lorenzo von Ritter, Michael E. Houle, Stephan Günnemann:
Intrinsic Degree: An Estimator of the Local Growth Rate in Graphs. SISAP 2018: 195-208 - 2017
- [c59]Manuel Then, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
Efficient Batched Distance and Centrality Computation in Unweighted and Weighted Graphs. BTW 2017: 247-266 - [c58]Linnea Passing, Manuel Then, Nina C. Hubig, Harald Lang, Michael Schreier, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
SQL- and Operator-centric Data Analytics in Relational Main-Memory Databases. EDBT 2017: 84-95 - [c57]Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann:
Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings. KDD 2017: 737-746 - [c56]Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos:
The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation. SDM 2017: 144-152 - [c55]Nina C. Hubig, Philip Fengler, Andreas Züfle, Ruixin Yang, Stephan Günnemann:
Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data. SSTD 2017: 300-316 - 2016
- [c54]Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos:
EdgeCentric: Anomaly Detection in Edge-Attributed Networks. ICDM Workshops 2016: 327-334 - [c53]Saskia Metzler, Stephan Günnemann, Pauli Miettinen:
Hyperbolae are No Hyperbole: Modelling Communities That are Not Cliques. ICDM 2016: 330-339 - [c52]Subhabrata Mukherjee, Stephan Günnemann, Gerhard Weikum:
Continuous Experience-aware Language Model. KDD 2016: 1075-1084 - [c51]Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos:
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. SDM 2016: 495-503 - 2015
- [c50]Jay Lee, Manzil Zaheer, Stephan Günnemann, Alexander J. Smola:
Preferential Attachment in Graphs with Affinities. AISTATS 2015 - [c49]Tobias Kötter, Stephan Günnemann, Michael R. Berthold, Christos Faloutsos:
Automatic Taxonomy Extraction from Bipartite Graphs. ICDM 2015: 221-230 - [c48]Manuel Then, Linnea Passing, Nina C. Hubig, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
Effiziente Integration von Data- und Graph-Mining-Algorithmen in relationale Datenbanksysteme. LWA 2015: 45-49 - [c47]Tobias Kötter, Stephan Günnemann, Christos Faloutsos, Michael R. Berthold:
Extracting Taxonomies from Bipartite Graphs. WWW (Companion Volume) 2015: 51-52 - 2014
- [c46]Stephan Günnemann, Ines Färber, Matthias Sebastian Rüdiger, Thomas Seidl:
SMVC: semi-supervised multi-view clustering in subspace projections. KDD 2014: 253-262 - [c45]Stephan Günnemann, Nikou Günnemann, Christos Faloutsos:
Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution. KDD 2014: 841-850 - [c44]Miguel Araujo, Spiros Papadimitriou, Stephan Günnemann, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra:
Com2: Fast Automatic Discovery of Temporal ('Comet') Communities. PAKDD (2) 2014: 271-283 - [c43]Tobias Kötter, Stephan Günnemann, Michael R. Berthold, Christos Faloutsos:
Fault-Tolerant Concept Detection in Information Networks. PAKDD (1) 2014: 410-421 - [c42]Miguel Araujo, Stephan Günnemann, Gonzalo Mateos, Christos Faloutsos:
Beyond Blocks: Hyperbolic Community Detection. ECML/PKDD (1) 2014: 50-65 - [c41]Nikou Günnemann, Stephan Günnemann, Christos Faloutsos:
Robust multivariate autoregression for anomaly detection in dynamic product ratings. WWW 2014: 361-372 - 2013
- [c40]Stephan Günnemann:
Subspace Clustering for Complex Data. BTW 2013: 343-362 - [c39]Stephan Günnemann, Christos Faloutsos:
Mixed Membership Subspace Clustering. ICDM 2013: 221-230 - [c38]Stephan Günnemann, Ines Färber, Sebastian Raubach, Thomas Seidl:
Spectral Subspace Clustering for Graphs with Feature Vectors. ICDM 2013: 231-240 - [c37]Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl:
An Evaluation Framework for Temporal Subspace Clustering Approaches. ICDM Workshops 2013: 1089-1092 - [c36]Jennifer H. Nguyen, Bo Hu, Stephan Günnemann, Martin Ester:
Finding contexts of social influence in online social networks. SNAKDD 2013: 1:1-1:9 - [c35]Geng Li, Stephan Günnemann, Mohammed J. Zaki:
Stochastic subspace search for top-k multi-view clustering. MultiClust@KDD 2013: 3 - [c34]Stephan Günnemann, Brigitte Boden, Ines Färber, Thomas Seidl:
Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors. PAKDD (1) 2013: 261-275 - [c33]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs. SSDBM 2013: 23:1-23:12 - [c32]Hardy Kremer, Stephan Günnemann, Simon Wollwage, Thomas Seidl:
Nesting the earth mover's distance for effective cluster tracing. SSDBM 2013: 34:1-34:4 - 2012
- [c31]Brigitte Boden, Stephan Günnemann, Thomas Seidl:
Tracing clusters in evolving graphs with node attributes. CIKM 2012: 2331-2334 - [c30]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDE 2012: 1207-1210 - [c29]Stephan Günnemann, Phuong Dao, Mohsen Jamali, Martin Ester:
Assessing the Significance of Data Mining Results on Graphs with Feature Vectors. ICDM 2012: 270-279 - [c28]Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl:
Effective and Robust Mining of Temporal Subspace Clusters. ICDM 2012: 369-378 - [c27]Stephan Günnemann, Hardy Kremer, Richard Musiol, Roman Haag, Thomas Seidl:
A Subspace Clustering Extension for the KNIME Data Mining Framework. ICDM Workshops 2012: 886-889 - [c26]Stephan Günnemann, Ines Färber, Thomas Seidl:
Multi-view clustering using mixture models in subspace projections. KDD 2012: 132-140 - [c25]Stephan Günnemann, Ines Färber, Kittipat Virochsiri, Thomas Seidl:
Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data. KDD 2012: 352-360 - [c24]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
Mining coherent subgraphs in multi-layer graphs with edge labels. KDD 2012: 1258-1266 - [c23]Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl:
Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases. PAKDD (1) 2012: 444-455 - [c22]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types. SSDBM 2012: 280-297 - 2011
- [c21]Emmanuel Müller, Ira Assent, Stephan Günnemann, Patrick Gerwert, Matthias Hannen, Timm Jansen, Thomas Seidl:
A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. BTW 2011: 347-366 - [c20]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl:
Scalable density-based subspace clustering. CIKM 2011: 1077-1086 - [c19]Stephan Günnemann, Ines Färber, Emmanuel Müller, Ira Assent, Thomas Seidl:
External evaluation measures for subspace clustering. CIKM 2011: 1363-1372 - [c18]Stephan Günnemann, Hardy Kremer, Dominik Lenhard, Thomas Seidl:
Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations. EDBT 2011: 237-248 - [c17]Stephan Günnemann, Emmanuel Müller, Sebastian Raubach, Thomas Seidl:
Flexible Fault Tolerant Subspace Clustering for Data with Missing Values. ICDM 2011: 231-240 - [c16]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
Finding Density-Based Subspace Clusters in Graphs with Feature Vectors. LWA 2011: 20-27 - [c15]Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl:
Tracing Evolving Clusters by Subspace and Value Similarity. PAKDD (2) 2011: 444-456 - [c14]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors. ECML/PKDD (1) 2011: 565-580 - [c13]Hardy Kremer, Stephan Günnemann, Anca Maria Ivanescu, Ira Assent, Thomas Seidl:
Efficient Processing of Multiple DTW Queries in Time Series Databases. SSDBM 2011: 150-167 - 2010
- [c12]Ira Assent, Hardy Kremer, Stephan Günnemann, Thomas Seidl:
Pattern detector: fast detection of suspicious stream patterns for immediate reaction. EDBT 2010: 709-712 - [c11]Hardy Kremer, Stephan Günnemann, Thomas Seidl:
Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques. ICDM Workshops 2010: 96-97 - [c10]Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl:
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. ICDM 2010: 845-850 - [c9]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDM 2010: 1220 - [c8]Stephan Günnemann, Hardy Kremer, Ines Färber, Thomas Seidl:
MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions. ICDM Workshops 2010: 1387-1390 - [c7]Stephan Günnemann, Thomas Seidl:
Subgraph Mining on Directed and Weighted Graphs. PAKDD (2) 2010: 133-146 - [c6]Stephan Günnemann, Hardy Kremer, Thomas Seidl:
Subspace Clustering for Uncertain Data. SDM 2010: 385-396 - [c5]Philipp Kranen, Stephan Günnemann, Sergej Fries, Thomas Seidl:
MC-Tree: Improving Bayesian Anytime Classification. SSDBM 2010: 252-269 - 2009
- [c4]Ira Assent, Stephan Günnemann, Hardy Kremer, Thomas Seidl:
High-Dimensional Indexing for Multimedia Features. BTW 2009: 187-206 - [c3]Stephan Günnemann, Emmanuel Müller, Ines Färber, Thomas Seidl:
Detection of orthogonal concepts in subspaces of high dimensional data. CIKM 2009: 1317-1326 - [c2]Emmanuel Müller, Ira Assent, Stephan Günnemann, Ralph Krieger, Thomas Seidl:
Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data. ICDM 2009: 377-386 - [c1]Emmanuel Müller, Ira Assent, Ralph Krieger, Stephan Günnemann, Thomas Seidl:
DensEst: Density Estimation for Data Mining in High Dimensional Spaces. SDM 2009: 175-186
Editorship
- 2011
- [e1]Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl:
Proceedings of the 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, Athens, Greece, September 5, 2011, in conjunction with ECML/PKDD 2011. CEUR Workshop Proceedings 772, CEUR-WS.org 2011 [contents]
Informal and Other Publications
- 2024
- [i129]Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, Stephan Günnemann:
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. CoRR abs/2402.09063 (2024) - [i128]Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann:
Attacking Large Language Models with Projected Gradient Descent. CoRR abs/2402.09154 (2024) - [i127]Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin:
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR abs/2402.15978 (2024) - [i126]Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan Günnemann:
Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy. CoRR abs/2403.04867 (2024) - [i125]Nicholas Gao, Stephan Günnemann:
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks. CoRR abs/2403.05249 (2024) - [i124]Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier:
Structurally Prune Anything: Any Architecture, Any Framework, Any Time. CoRR abs/2403.18955 (2024) - 2023
- [i123]Morgane Ayle, Jan Schuchardt, Lukas Gosch, Daniel Zügner, Stephan Günnemann:
Training Differentially Private Graph Neural Networks with Random Walk Sampling. CoRR abs/2301.00738 (2023) - [i122]Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann:
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. CoRR abs/2301.02039 (2023) - [i121]Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski:
Are Defenses for Graph Neural Networks Robust? CoRR abs/2301.13694 (2023) - [i120]Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. CoRR abs/2302.00049 (2023) - [i119]Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann:
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. CoRR abs/2302.02829 (2023) - [i118]Nicholas Gao, Stephan Günnemann:
Generalizing Neural Wave Functions. CoRR abs/2302.04168 (2023) - [i117]Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. CoRR abs/2303.04791 (2023) - [i116]Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann:
Training, Architecture, and Prior for Deterministic Uncertainty Methods. CoRR abs/2303.05796 (2023) - [i115]Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann:
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection. CoRR abs/2303.14961 (2023) - [i114]Johannes Getzner, Bertrand Charpentier, Stephan Günnemann:
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. CoRR abs/2304.00897 (2023) - [i113]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR abs/2304.02902 (2023) - [i112]Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz:
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. CoRR abs/2305.00472 (2023) - [i111]Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann:
Revisiting Robustness in Graph Machine Learning. CoRR abs/2305.00851 (2023) - [i110]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. CoRR abs/2305.10498 (2023) - [i109]Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J. Theis, Stephan Günnemann:
MAGNet: Motif-Agnostic Generation of Molecules from Shapes. CoRR abs/2305.19303 (2023) - [i108]Marten Lienen, Jan Hansen-Palmus, David Lüdke, Stephan Günnemann:
Generative Diffusion for 3D Turbulent Flows. CoRR abs/2306.01776 (2023) - [i107]Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. CoRR abs/2306.14916 (2023) - [i106]Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Adversarial Training for Graph Neural Networks. CoRR abs/2306.15427 (2023) - [i105]Johanna Sommer, Leon Hetzel, David Lüdke, Fabian J. Theis, Stephan Günnemann:
The power of motifs as inductive bias for learning molecular distributions. CoRR abs/2306.17246 (2023) - [i104]Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel:
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly. CoRR abs/2307.01317 (2023) - [i103]Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann:
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. CoRR abs/2307.04533 (2023) - [i102]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:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i101]Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS). CoRR abs/2308.05239 (2023) - [i100]Francesco Campi, Lukas Gosch, Tom Wollschläger, Yan Scholten, Stephan Günnemann:
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness. CoRR abs/2308.08173 (2023) - [i99]Sebastian Schmidt, Stephan Günnemann:
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. CoRR abs/2309.05517 (2023) - [i98]Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann:
Assessing Robustness via Score-Based Adversarial Image Generation. CoRR abs/2310.04285 (2023) - [i97]Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Hierarchical Randomized Smoothing. CoRR abs/2310.16221 (2023) - [i96]Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel:
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. CoRR abs/2310.19737 (2023) - [i95]David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann:
Add and Thin: Diffusion for Temporal Point Processes. CoRR abs/2311.01139 (2023) - [i94]Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel:
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRR abs/2311.06481 (2023) - [i93]Filippo Guerranti, Zinuo Yi, Anna Starovoit, Rafiq Kamel, Simon Geisler, Stephan Günnemann:
On the Adversarial Robustness of Graph Contrastive Learning Methods. CoRR abs/2311.17853 (2023) - [i92]Jan Schuchardt, Yan Scholten, Stephan Günnemann:
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. CoRR abs/2312.02708 (2023) - [i91]Michael Plainer, Hannes Stärk, Charlotte Bunne, Stephan Günnemann:
Transition Path Sampling with Boltzmann Generator-based MCMC Moves. CoRR abs/2312.05340 (2023) - [i90]Ege Erdogan, Simon Geisler, Stephan Günnemann:
Poisoning × Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks. CoRR abs/2312.05502 (2023) - 2022
- [i89]Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann:
Multi-Objective Model Selection for Time Series Forecasting. CoRR abs/2202.08485 (2022) - [i88]Tong Zhao, Gang Liu, Stephan Günnemann, Meng Jiang:
Graph Data Augmentation for Graph Machine Learning: A Survey. CoRR abs/2202.08871 (2022) - [i87]Armin Moin, Ukrit Wattanavaekin, Alexandra Lungu, Moharram Challenger, Atta Badii, Stephan Günnemann:
Enabling Automated Machine Learning for Model-Driven AI Engineering. CoRR abs/2203.02927 (2022) - [i86]Bertrand Charpentier, Simon Kibler, Stephan Günnemann:
Differentiable DAG Sampling. CoRR abs/2203.08509 (2022) - [i85]Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher, Stephan Günnemann:
Is it all a cluster game? - Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space. CoRR abs/2203.08549 (2022) - [i84]Marten Lienen, Stephan Günnemann:
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. CoRR abs/2203.08852 (2022) - [i83]Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary W. Ulissi, C. Lawrence Zitnick, Abhishek Das:
How Do Graph Networks Generalize to Large and Diverse Molecular Systems? CoRR abs/2204.02782 (2022) - [i82]Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis:
Predicting single-cell perturbation responses for unseen drugs. CoRR abs/2204.13545 (2022) - [i81]Nicholas Gao, Stephan Günnemann:
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. CoRR abs/2205.14962 (2022) - [i80]Bertrand Charpentier, Ransalu Senanayake, Mykel J. Kochenderfer, Stephan Günnemann:
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning. CoRR abs/2206.01558 (2022) - [i79]John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann:
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. CoRR abs/2206.10451 (2022) - [i78]Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann:
On the Robustness and Anomaly Detection of Sparse Neural Networks. CoRR abs/2207.04227 (2022) - [i77]Jonathan Külz, Andreas Spitz, Ahmad Abu-Akel, Stephan Günnemann, Robert West:
United States Politicians' Tone Became More Negative with 2016 Primary Campaigns. CoRR abs/2207.08112 (2022) - [i76]Jörg Christian Kirchhof, Evgeny Kusmenko, Jonas Ritz, Bernhard Rumpe, Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat. CoRR abs/2209.07282 (2022) - [i75]Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie:
A Systematic Evaluation of Node Embedding Robustness. CoRR abs/2209.08064 (2022) - [i74]Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie:
Unveiling the Sampling Density in Non-Uniform Geometric Graphs. CoRR abs/2210.08219 (2022) - [i73]Marin Bilos, Emanuel Ramneantu, Stephan Günnemann:
Irregularly-Sampled Time Series Modeling with Spline Networks. CoRR abs/2210.10630 (2022) - [i72]Marten Lienen, Stephan Günnemann:
torchode: A Parallel ODE Solver for PyTorch. CoRR abs/2210.12375 (2022) - [i71]Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann:
Localized Randomized Smoothing for Collective Robustness Certification. CoRR abs/2210.16140 (2022) - [i70]Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Process Diffusion. CoRR abs/2211.02590 (2022) - [i69]Jan Schuchardt, Stephan Günnemann:
Invariance-Aware Randomized Smoothing Certificates. CoRR abs/2211.14207 (2022) - [i68]Johannes Gasteiger, Chendi Qian, Stephan Günnemann:
Influence-Based Mini-Batching for Graph Neural Networks. CoRR abs/2212.09083 (2022) - [i67]Martin Grohe, Stephan Günnemann, Stefanie Jegelka, Christopher Morris:
Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). Dagstuhl Reports 12(3): 141-155 (2022) - 2021
- [i66]Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann:
Language-Agnostic Representation Learning of Source Code from Structure and Context. CoRR abs/2103.11318 (2021) - [i65]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann:
Neural Temporal Point Processes: A Review. CoRR abs/2104.03528 (2021) - [i64]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. CoRR abs/2105.04471 (2021) - [i63]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann:
Detecting Anomalous Event Sequences with Temporal Point Processes. CoRR abs/2106.04465 (2021) - [i62]Johannes Gasteiger, Florian Becker, Stephan Günnemann:
GemNet: Universal Directional Graph Neural Networks for Molecules. CoRR abs/2106.08903 (2021) - [i61]Armin Moin, Atta Badii, Stephan Günnemann:
A Model-Driven Engineering Approach to Machine Learning and Software Modeling. CoRR abs/2107.02689 (2021) - [i60]Armin Moin, Atta Badii, Stephan Günnemann:
Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications. CoRR abs/2107.02690 (2021) - [i59]Armin Moin, Andrei Mituca, Atta Badii, Stephan Günnemann:
ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. CoRR abs/2107.02692 (2021) - [i58]Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann:
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering. CoRR abs/2107.06325 (2021) - [i57]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence. CoRR abs/2107.06708 (2021) - [i56]Johannes Klicpera, Marten Lienen, Stephan Günnemann:
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. CoRR abs/2107.06876 (2021) - [i55]Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
On Out-of-distribution Detection with Energy-based Models. CoRR abs/2107.08785 (2021) - [i54]Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp:
OODformer: Out-Of-Distribution Detection Transformer. CoRR abs/2107.08976 (2021) - [i53]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:
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph). CoRR abs/2108.13233 (2021) - [i52]Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich:
On Second-order Optimization Methods for Federated Learning. CoRR abs/2109.02388 (2021) - [i51]Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus:
A Study of Joint Graph Inference and Forecasting. CoRR abs/2109.04979 (2021) - [i50]Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò:
3D Infomax improves GNNs for Molecular Property Prediction. CoRR abs/2110.04126 (2021) - [i49]Nicholas Gao, Stephan Günnemann:
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. CoRR abs/2110.05064 (2021) - [i48]Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann:
Intriguing Properties of Input-dependent Randomized Smoothing. CoRR abs/2110.05365 (2021) - [i47]Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. CoRR abs/2110.10942 (2021) - [i46]Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann:
Neural Flows: Efficient Alternative to Neural ODEs. CoRR abs/2110.13040 (2021) - [i45]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR abs/2110.14012 (2021) - [i44]Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann:
Robustness of Graph Neural Networks at Scale. CoRR abs/2110.14038 (2021) - [i43]Johannes Klicpera, Chandan Yeshwanth, Stephan Günnemann:
Directional Message Passing on Molecular Graphs via Synthetic Coordinates. CoRR abs/2111.04718 (2021) - 2020
- [i42]Johannes Klicpera, Janek Groß, Stephan Günnemann:
Directional Message Passing for Molecular Graphs. CoRR abs/2003.03123 (2020) - [i41]Zhen Han, Yuyi Wang, Yunpu Ma, Stephan Günnemann, Volker Tresp:
Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs. CoRR abs/2003.13432 (2020) - [i40]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. CoRR abs/2006.09239 (2020) - [i39]Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann:
Fast and Flexible Temporal Point Processes with Triangular Maps. CoRR abs/2006.12631 (2020) - [i38]Marcel Hildebrandt, Hang Li, Rajat Koner, Volker Tresp, Stephan Günnemann:
Scene Graph Reasoning for Visual Question Answering. CoRR abs/2007.01072 (2020) - [i37]Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann:
Scaling Graph Neural Networks with Approximate PageRank. CoRR abs/2007.01570 (2020) - [i36]Nick Harmening, Marin Bilos, Stephan Günnemann:
Deep Representation Learning and Clustering of Traffic Scenarios. CoRR abs/2007.07740 (2020) - [i35]Anna-Kathrin Kopetzki, Stephan Günnemann:
Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training. CoRR abs/2007.14120 (2020) - [i34]Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. CoRR abs/2008.12952 (2020) - [i33]Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann:
From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2). CoRR abs/2009.10632 (2020) - [i32]Armin Moin, Stephan Rössler, Stephan Günnemann:
ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning. CoRR abs/2009.10633 (2020) - [i31]Marin Bilos, Stephan Günnemann:
Equivariant Normalizing Flows for Point Processes and Sets. CoRR abs/2010.03242 (2020) - [i30]Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann:
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? CoRR abs/2010.14986 (2020) - [i29]Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. CoRR abs/2010.15651 (2020) - [i28]Johannes Klicpera, Shankari Giri, Johannes T. Margraf, Stephan Günnemann:
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. CoRR abs/2011.14115 (2020) - 2019
- [i27]Daniel Zügner, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks via Meta Learning. CoRR abs/1902.08412 (2019) - [i26]Subhabrata Mukherjee, Stephan Günnemann:
GhostLink: Latent Network Inference for Influence-aware Recommendation. CoRR abs/1905.05955 (2019) - [i25]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness and Robust Training for Graph Convolutional Networks. CoRR abs/1906.12269 (2019) - [i24]Oleksandr Shchur, Marin Bilos, Stephan Günnemann:
Intensity-Free Learning of Temporal Point Processes. CoRR abs/1909.12127 (2019) - [i23]Oleksandr Shchur, Stephan Günnemann:
Overlapping Community Detection with Graph Neural Networks. CoRR abs/1909.12201 (2019) - [i22]Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke:
Group Centrality Maximization for Large-scale Graphs. CoRR abs/1910.13874 (2019) - [i21]Aleksandar Bojchevski, Stephan Günnemann:
Certifiable Robustness to Graph Perturbations. CoRR abs/1910.14356 (2019) - [i20]Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann:
Diffusion Improves Graph Learning. CoRR abs/1911.05485 (2019) - [i19]Marin Bilos, Bertrand Charpentier, Stephan Günnemann:
Uncertainty on Asynchronous Time Event Prediction. CoRR abs/1911.05503 (2019) - [i18]Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann:
Oktoberfest Food Dataset. CoRR abs/1912.05007 (2019) - 2018
- [i17]Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann:
NetGAN: Generating Graphs via Random Walks. CoRR abs/1803.00816 (2018) - [i16]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. CoRR abs/1805.07984 (2018) - [i15]Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein:
Dual-Primal Graph Convolutional Networks. CoRR abs/1806.00770 (2018) - [i14]Aleksandar Bojchevski, Stephan Günnemann:
Adversarial Attacks on Node Embeddings. CoRR abs/1809.01093 (2018) - [i13]Roberto Alonso, Stephan Günnemann:
Mining Contrasting Quasi-Clique Patterns. CoRR abs/1810.01836 (2018) - [i12]Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann:
Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank. CoRR abs/1810.05997 (2018) - [i11]Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. CoRR abs/1810.11953 (2018) - [i10]Richard Kurle, Stephan Günnemann, Patrick van der Smagt:
Multi-Source Neural Variational Inference. CoRR abs/1811.04451 (2018) - [i9]Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann:
Pitfalls of Graph Neural Network Evaluation. CoRR abs/1811.05868 (2018) - 2017
- [i8]Subhabrata Mukherjee, Stephan Günnemann, Gerhard Weikum:
Personalized Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion. CoRR abs/1705.02669 (2017) - [i7]Aleksandar Bojchevski, Stephan Günnemann:
Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking. CoRR abs/1707.03815 (2017) - [i6]Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann:
Introduction to Tensor Decompositions and their Applications in Machine Learning. CoRR abs/1711.10781 (2017) - 2016
- [i5]Saskia Metzler, Stephan Günnemann, Pauli Miettinen:
Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques. CoRR abs/1602.04650 (2016) - 2015
- [i4]Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos:
EdgeCentric: Anomaly Detection in Edge-Attributed Networks. CoRR abs/1510.05544 (2015) - [i3]Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos:
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. CoRR abs/1511.06030 (2015) - 2014
- [i2]Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, Christos Faloutsos:
Linearized and Turbo Belief Propagation. CoRR abs/1406.7288 (2014) - [i1]Stephan Günnemann, Hardy Kremer, Matthias Hannen, Thomas Seidl:
KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks. CoRR abs/1407.3850 (2014)
Coauthor Index
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