Stephan Günnemann

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Aleksandar BojchevskiUniversity of CologneVerified email at uni-koeln.de
Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, GermanyVerified email at dbs.ifi.lmu.de
Johannes Gasteiger, né KlicperaGoogle ResearchVerified email at google.com
Daniel ZügnerMicrosoft ResearchVerified email at microsoft.com
Oleksandr ShchurApplied Scientist, AWS AIVerified email at amazon.com
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundVerified email at cs.tu-dortmund.de
Bertrand CharpentierTechnical University of MunichVerified email at in.tum.de
Simon GeislerTechnical University of MunichVerified email at in.tum.de
Christos FaloutsosCMUVerified email at cs.cmu.edu
Ines FärberData Management and Data ExplorationVerified email at cs.rwth-aachen.de
Ira AssentProfessor of Computer Science, Aarhus University, Denmark; IAS-8, Forschungszentrum JülichVerified email at cs.au.dk
Hardy KremerDeloitte Analytics Institute, GermanyVerified email at deloitte.de
Marin BilošPhD student, Technical University of MunichVerified email at in.tum.de
Karsten RoscherResearcher, Fraunhofer IKSVerified email at iks.fraunhofer.de
Thomas NeumannProfessor, Technische Universität MünchenVerified email at in.tum.de
Gitta KutyniokBavarian AI Chair for Mathematical Foundations of Artificial Intelligence, LMU MunichVerified email at math.lmu.de
Nicholas GaoTechnical University of MunichVerified email at tum.de
Armin MoinAssistant Professor of Computer Science at the University of Colorado Colorado Springs (UCCS)Verified email at uccs.edu
Jan SchuchardtTechnical University of MunichVerified email at in.tum.de
Volker TrespLudwig-Maximilians-Universität München (LMU Munich)Verified email at dbs.ifi.lmu.de

Stephan Günnemann

Professor of Computer Science, Technical University of Munich

Verified email at in.tum.de - Homepage

Machine Learning Graphs Graph Neural Networks Robustness


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ChatGPT for good? On opportunities and challenges of large language models for education E Kasneci, K Seßler, S Küchemann, M Bannert, D Dementieva, F Fischer, ... Learning and individual differences 103, 102274, 2023	2900	2023
Predict then propagate: Graph neural networks meet personalized pagerank J Gasteiger, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2019	2042	2019
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NeurIPS, 2018	1371	2018
Adversarial attacks on neural networks for graph data D Zügner, A Akbarnejad, S Günnemann ACM SIGKDD International Conference on Knowledge Discovery & Data Mining …, 2018	1110	2018
Directional message passing for molecular graphs J Gasteiger, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	905	2020
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	760	2018
Diffusion improves graph learning J Gasteiger, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	744	2019
Adversarial Attacks on Graph Neural Networks via Meta Learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	674*	2019
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann International Conference on Machine Learning (ICML), 2018	462	2018
Gemnet: Universal directional graph neural networks for molecules J Gasteiger, F Becker, S Günnemann Advances in Neural Information Processing Systems 34, 6790-6802, 2021	446*	2021
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	385	2018
Evaluating clustering in subspace projections of high dimensional data E Müller, S Günnemann, I Assent, T Seidl Proceedings of the VLDB Endowment 2 (1), 1270-1281, 2009	364	2009
Fast and uncertainty-aware directional message passing for non-equilibrium molecules J Gasteiger, S Giri, JT Margraf, S Günnemann Machine Learning for Molecules Workshop, NeurIPS, 2020	357	2020
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning (ICML), 695-704, 2019	354	2019
Scaling graph neural networks with approximate pagerank A Bojchevski, J Gasteiger, B Perozzi, A Kapoor, M Blais, B Rózemberczki, ... Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020	298	2020
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	282	2017
3d infomax improves gnns for molecular property prediction H Stärk, D Beaini, G Corso, P Tossou, C Dallago, S Günnemann, P Liò International Conference on Machine Learning, 20479-20502, 2022	207	2022
Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts B Charpentier, D Zügner, S Günnemann Neural Information Processing Systems (NeurIPS), 2020	186	2020
Certifiable robustness and robust training for graph convolutional networks D Zügner, S Günnemann Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	176	2019
Intensity-free learning of temporal point processes O Shchur, M Biloš, S Günnemann International Conference on Learning Representations (ICLR), 2019	171	2019

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