| 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 | 891 | 2018 |
| GluonTS: Probabilistic and Neural Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... The Journal of Machine Learning Research 21 (1), 4629-4634, 2020 | 272 | 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 | 223 | 2022 |
| Chronos: Learning the language of time series AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ... Transactions on Machine Learning Research, 2835-8856, 2024 | 178 | 2024 |
| Forward-backward quasi-Newton methods for nonsmooth optimization problems L Stella, A Themelis, P Patrinos Computational Optimization and Applications 67 (3), 443–487, 2017 | 164 | 2017 |
| A simple and efficient algorithm for nonlinear model predictive control L Stella, A Themelis, P Sopasakis, P Patrinos 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 1939-1944, 2017 | 151 | 2017 |
| 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 | 146 | 2020 |
| Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone linesearch algorithms A Themelis, L Stella, P Patrinos SIAM Journal on Optimization 28 (3), 2274-2303, 2018 | 141 | 2018 |
| Gluonts: Probabilistic Time Series Models in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 139 | 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 | 132 | 2020 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240 6, 2020 | 127 | 2020 |
| Douglas-Rachford splitting: Complexity estimates and accelerated variants P Patrinos, L Stella, A Bemporad 53rd IEEE Conference on Decision and Control, 4234-4239, 2014 | 101 | 2014 |
| Modeling cellular compartmentation in one‐carbon metabolism M Scotti, L Stella, EJ Shearer, PJ Stover Wiley Interdisciplinary Reviews: Systems Biology and Medicine 5 (3), 343-365, 2013 | 66 | 2013 |
| Forward-backward truncated Newton methods for convex composite optimization P Patrinos, L Stella, A Bemporad arXiv preprint arXiv:1402.6655, 2014 | 50 | 2014 |
| Proximal gradient algorithms: Applications in signal processing N Antonello, L Stella, P Patrinos, T Van Waterschoot arXiv preprint arXiv:1803.01621, 2018 | 39 | 2018 |
| Artificial intelligence system combining state space models and neural networks for time series forecasting S Rangapuram, JA Gasthaus, T Januschowski, M Seeger, L Stella US Patent 11,281,969, 2022 | 31 | 2022 |
| New primal-dual proximal algorithm for distributed optimization P Latafat, L Stella, P Patrinos 2016 IEEE 55th Conference on Decision and Control (CDC), 1959-1964, 2016 | 22 | 2016 |
| Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient P Latafat, A Themelis, L Stella, P Patrinos Mathematical Programming, 1-39, 2024 | 21 | 2024 |
| Newton-type alternating minimization algorithm for convex optimization L Stella, A Themelis, P Patrinos IEEE Transactions on Automatic Control 64 (2), 697-711, 2019 | 18 | 2019 |
| Systems, apparatuses, and methods for anomaly detection J Gasthaus, MEF Ayed, L Stella, T Januschowski US Patent 11,675,646, 2023 | 16 | 2023 |
Tim JanuschowskiDatabricks (prior: Zalando, AWS/Amazon, TU Berlin, University College Cork, Zuse Institute Berlin)Email verificata su databricks.com