| Pitfalls of Graph Neural Network Evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NeurIPS 2018, 2018 | 1552 | 2018 |
| NetGAN: Generating Graphs via Random Walks A Bojchevski, O Shchur, D Zügner, S Günnemann International Conference on Machine Learning (ICML), 2018 | 483 | 2018 |
| Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017 | 296 | 2017 |
| Intensity-Free Learning of Temporal Point Processes O Shchur, M Biloš, S Günnemann International Conference on Learning Representations (ICLR), 2020 | 192 | 2020 |
| Chronos: Learning the language of time series AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ... arXiv preprint arXiv:2403.07815, 2024 | 178 | 2024 |
| Overlapping Community Detection with Graph Neural Networks O Shchur, S Günnemann Deep Learning on Graphs Workshop, KDD 2019, 2019 | 153 | 2019 |
| Neural Temporal Point Processes: A Review O Shchur, AC Türkmen, T Januschowski, S Günnemann International Joint Conference on Artificial Intelligence (IJCAI), 2021 | 113 | 2021 |
| Dual-primal graph convolutional networks F Monti, O Shchur, A Bojchevski, O Litany, S Günnemann, MM Bronstein Graph Embedding and Mining Workshop, ECML-PKDD 2019, 2018 | 112* | 2018 |
| Fast and Flexible Temporal Point Processes with Triangular Maps O Shchur, N Gao, M Biloš, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2020 | 39 | 2020 |
| AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting O Shchur, C Turkmen, N Erickson, H Shen, A Shirkov, T Hu, Y Wang AutoML Conference, 2023 | 38 | 2023 |
| Using deep learning for flexible and scalable earthquake forecasting K Dascher‐Cousineau, O Shchur, EE Brodsky, S Günnemann Geophysical Research Letters 50 (17), e2023GL103909, 2023 | 19 | 2023 |
| Detecting Anomalous Event Sequences with Temporal Point Processes O Shchur, AC Türkmen, T Januschowski, J Gasthaus, S Günnemann Neural Information Processing Systems (NeurIPS), 2021 | 17 | 2021 |
| Add and Thin: Diffusion for Temporal Point Processes D Lüdke, M Biloš, O Shchur, M Lienen, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2023 | 8 | 2023 |
| Anomaly detection in car-booking graphs O Shchur, A Bojchevski, M Farghal, S Günnemann, Y Saber 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 604-607, 2018 | 7 | 2018 |
| Modeling Continuous-time Event Data with Neural Temporal Point Processes O Shchur Technische Universität München, 2022 | 2 | 2022 |
| Forecasting algorithms for intelligent resource scaling: An experimental analysis Y Diao, D Horn, A Kipf, O Shchur, I Benito, W Dong, D Pagano, P Pfeil, ... Proceedings of the 2024 ACM Symposium on Cloud Computing, 126-143, 2024 | | 2024 |
| Are size and timing within earthquake sequences separable? K Dascher-Cousineau, B Emily, O Shchur AGU Fall Meeting Abstracts 2022, S56A-05, 2022 | | 2022 |
| Quantifying Causal Contribution in Rare Event Data AC Turkmen, D Janzing, O Shchur, L Minorics, L Callot A causal view on dynamical systems, NeurIPS 2022 workshop, 0 | | |
Stephan GünnemannProfessor of Computer Science, Technical University of MunichEmail verificata su in.tum.de