| Deep state space models for time series forecasting SS Rangapuram, M Seeger, J Gasthaus, L Stella, Y Wang, ... Proceedings of the 32nd international conference on neural information …, 2018 | 126 | 2018 |
| Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions Y Wang, R Khardon, D Pechyony, R Jones Annual Conference on Learning Theory, 2012 | 73 | 2012 |
| Probabilistic Demand Forecasting at Scale YW Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski ... Proceedings of the VLDB Endowment 10 (12), 1694-1705, 2017 | 54* | 2017 |
| Random Matrix Theory and Its Innovative Applications A Edelman, Y Wang Advances in Applied Mathematics, Modeling, and Computational Science, 91-116, 2012 | 42 | 2012 |
| 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 | 41* | 2020 |
| Deep Factors for Forecasting Y Wang, A Smola, DC Maddix, J Gasthaus, D Foster, T Januschowski ICML 2019, arXiv preprint arXiv:1905.12417, 2019 | 34 | 2019 |
| 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 | 34 | 2019 |
| Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 31 | 2020 |
| Forecasting big time series: old and new C Faloutsos, J Gasthaus, T Januschowski, Y Wang Proceedings of the VLDB Endowment 11 (12), 2102-2105, 2018 | 29 | 2018 |
| Sparse Variational Inference for Generalized Gaussian Process Models R Sheth, Y Wang, R Khardon International Conference on Machine Learning, 2015 | 28 | 2015 |
| Nonparametric Bayesian estimation of periodic light curves Y Wang, R Khardon, P Protopapas The Astrophysical Journal 756 (1), 67, 2012 | 28 | 2012 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240, 2020 | 18 | 2020 |
| Elastic Machine Learning Algorithms in Amazon SageMaker E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ... SIGMOD, 2020 | 14 | 2020 |
| Sparse Gaussian processes for multi-task learning Y Wang, R Khardon Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012 | 14 | 2012 |
| Shift-invariant grouped multi-task learning for Gaussian processes Y Wang, R Khardon, P Protopapas Joint European Conference on Machine Learning and Knowledge Discovery in …, 2010 | 13 | 2010 |
| Deep factors with gaussian processes for forecasting DC Maddix, Y Wang, A Smola NeurIPS Workshop on Bayesian Deep Learning, 2018 | 11 | 2018 |
| Random matrix theory, numerical computation and applications A Edelman, BD Sutton, Y Wang Modern Aspects of Random Matrix Theory 72, 53, 2014 | 10 | 2014 |
| 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 |
| Forecasting big time series: Theory and practice C Faloutsos, V Flunkert, J Gasthaus, T Januschowski, Y Wang Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 8 | 2019 |
| 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 |
Tim JanuschowskiAmazonVerified email at amazon.com
