| 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, 2018 | 330 | 2018 |
| The total variation on hypergraphs-learning on hypergraphs revisited M Hein, S Setzer, L Jost, SS Rangapuram Advances in Neural Information Processing Systems 26, 2013 | 116 | 2013 |
| Constrained 1-spectral clustering SS Rangapuram, M Hein Artificial Intelligence and Statistics, 1143-1151, 2012 | 102 | 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 | 94 | 2013 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240, 2020 | 92 | 2020 |
| 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 | 92 | 2019 |
| GluonTS: Probabilistic and Neural Time Series Modeling in Python. A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... J. Mach. Learn. Res. 21 (116), 1-6, 2020 | 88 | 2020 |
| 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 | 59 | 2020 |
| Gluonts: Probabilistic time series models in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 57 | 2019 |
| 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, 2995-3007, 2020 | 32 | 2020 |
| 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 | 21 | 2013 |
| End-to-end learning of coherent probabilistic forecasts for hierarchical time series SS Rangapuram, LD Werner, K Benidis, P Mercado, J Gasthaus, ... International Conference on Machine Learning, 8832-8843, 2021 | 19 | 2021 |
| Tight continuous relaxation of the balanced k-cut problem SS Rangapuram, PK Mudrakarta, M Hein Advances in Neural Information Processing Systems 27, 2014 | 18 | 2014 |
| 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, 15371-15382, 2020 | 17 | 2020 |
| 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 | 17 | 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 | 13 | 2018 |
| Methods for sparse and low-rank recovery under simplex constraints P Li, SS Rangapuram, M Slawski arXiv preprint arXiv:1605.00507, 2016 | 11 | 2016 |
| GluonTS: Probabilistic Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 10 | 2019 |
| Neural Flows: Efficient Alternative to Neural ODEs M Biloš, J Sommer, SS Rangapuram, T Januschowski, S Günnemann Advances in Neural Information Processing Systems 34, 21325-21337, 2021 | 6 | 2021 |
| Deep learning for forecasting T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot Foresight: The International Journal of Applied Forecasting, 35-41, 2018 | 5 | 2018 |
Tim JanuschowskiZalando (formerly: AWS, Amazon, TU Berlin, University College Cork, Zuse Institute Berlin)Verified email at amazon.com
