| 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 | 360* | 2020 |
| Deep learning for time series forecasting: Tutorial and literature survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys 55 (6), 1-36, 2022 | 286* | 2022 |
| 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 | 184 | 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 | 120 | 2020 |
| Sparse portfolios for high-dimensional financial index tracking K Benidis, Y Feng, DP Palomar IEEE Transactions on signal processing 66 (1), 155-170, 2017 | 104 | 2017 |
| 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 | 78 | 2021 |
| Orthogonal sparse PCA and covariance estimation via procrustes reformulation K Benidis, Y Sun, P Babu, DP Palomar IEEE Transactions on Signal Processing 64 (23), 6211-6226, 2016 | 59 | 2016 |
| Optimization methods for financial index tracking: From theory to practice K Benidis, Y Feng, DP Palomar Foundations and Trends® in Optimization 3 (3), 171-279, 2018 | 42 | 2018 |
| Multivariate quantile function forecaster K Kan, FX Aubet, T Januschowski, Y Park, K Benidis, L Ruthotto, ... International Conference on Artificial Intelligence and Statistics, 10603-10621, 2022 | 26 | 2022 |
| Deep explicit duration switching models for time series AF Ansari, K Benidis, R Kurle, AC Turkmen, H Soh, AJ Smola, B Wang, ... Advances in Neural Information Processing Systems 34, 29949-29961, 2021 | 25 | 2021 |
| Orthogonal sparse eigenvectors: A procrustes problem K Benidis, Y Sun, P Babu, DP Palomar 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 6 | 2016 |
| Solving recurrent MIPs with semi-supervised graph neural networks K Benidis, U Rosolia, S Rangapuram, G Iosifidis, G Paschos arXiv preprint arXiv:2302.11992, 2023 | 2 | 2023 |
| Probabilistic forecasting with nonparametric quantile functions J Gasthaus, K Benidis, W Yuyang, D Salinas, V Flunkert US Patent 11,531,917, 2022 | 1 | 2022 |
| Bidirectional relay communication with additional private message K Benidis, RF Schaefer, H Boche 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless …, 2013 | 1 | 2013 |
| A flexible forecasting stack T Januschowski, J Gasthaus, YB Wang, S Rangapuram, C Turkmen, ... | | 2024 |
| Middle-mile optimization for next-day delivery K Benidis, G Paschos, M Gross, G Iosifidis arXiv preprint arXiv:2310.18388, 2023 | | 2023 |
| GluonTS: Probabilistic Time Series Models in Python V Flunkert, A Alexandrov, J Schulz, J Gasthaus, D Salinas, DM Robinson, ... | | 2019 |
| Design of Portfolio of Stocks to Track an Index K Benidis, DP Palomar | | 2018 |
| High-dimensional sparsity methods in machine learning and finance K Benidis | | 2018 |
| Computing Sparse Eigenvectors of a Matrix K Benidis, DP Palomar | | 2017 |
Tim JanuschowskiDatabricks (prior: Zalando, AWS/Amazon, TU Berlin, University College Cork, Zuse Institute Berlin)Verified email at databricks.com
