| Deep state space models for time series forecasting SS Rangapuram, MW Seeger, J Gasthaus, L Stella, Y Wang, ... Advances in neural information processing systems 31, 7785-7794, 2018 | 118 | 2018 |
| Constrained 1-Spectral Clustering. SS Rangapuram, M Hein AISTATS 30, 90, 2012 | 96 | 2012 |
| Towards realistic team formation in social networks based on densest subgraphs SS Rangapuram, T Bühler, M Hein Proceedings of the 22nd international conference on World Wide Web, 1077-1088, 2013 | 84 | 2013 |
| The total variation on hypergraphs-learning on hypergraphs revisited M Hein, S Setzer, L Jost, SS Rangapuram Advances in Neural Information Processing Systems 26, 2427-2435, 2013 | 84 | 2013 |
| Probabilistic forecasting with spline quantile function rnns J Gasthaus, K Benidis, Y Wang, SS Rangapuram, D Salinas, V Flunkert, ... The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 30 | 2019 |
| Gluonts: Probabilistic time series models in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 24 | 2019 |
| Constrained fractional set programs and their application in local clustering and community detection T Bühler, SS Rangapuram, S Setzer, M Hein International Conference on Machine Learning, 624-632, 2013 | 17 | 2013 |
| Tight continuous relaxation of the balanced k-cut problem SS Rangapuram, PK Mudrakarta, M Hein Advances in Neural Information Processing Systems 27, 3131-3139, 2014 | 16 | 2014 |
| Elastic Machine Learning Algorithms in Amazon SageMaker E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ... Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 14 | 2020 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240, 2020 | 13 | 2020 |
| Methods for sparse and low-rank recovery under simplex constraints P Li, SS Rangapuram, M Slawski arXiv preprint arXiv:1605.00507, 2016 | 11 | 2016 |
| Approximate bayesian inference in linear state space models for intermittent demand forecasting at scale M Seeger, S Rangapuram, Y Wang, D Salinas, J Gasthaus, ... arXiv preprint arXiv:1709.07638, 2017 | 9 | 2017 |
| Deep Learning for Forecasting: Current Trends and Challenges. T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot Foresight: The International Journal of Applied Forecasting, 2018 | 8 | 2018 |
| GluonTS: Probabilistic Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 6 | 2019 |
| GluonTS: Probabilistic and Neural Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... Journal of Machine Learning Research 21 (116), 1-6, 2020 | 4 | 2020 |
| Deep learning for forecasting T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot Foresight: The International Journal of Applied Forecasting, 35-41, 2018 | 3 | 2018 |
| Deep Rao-Blackwellised Particle Filters for Time Series Forecasting R Kurle, SS Rangapuram, E de Bézenac, S Günnemann, J Gasthaus Advances in Neural Information Processing Systems 33, 2020 | | 2020 |
| Normalizing Kalman Filters for Multivariate Time Series Analysis E de Bézenac, SS Rangapuram, K Benidis, M Bohlke-Schneider, R Kurle, ... Advances in Neural Information Processing Systems 33, 2020 | | 2020 |
| Graph-based methods for unsupervised and semi-supervised data analysis SS Rangapuram | | 2016 |
| Neural time series models with GluonTS Time Series Workshop ICML 2019 A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... | | |
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