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Lorenzo Stella
Lorenzo Stella
Amazon, AWS AI
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Zitiert von
Zitiert von
Jahr
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
6232018
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
1762020
Forward-backward quasi-Newton methods for nonsmooth optimization problems
L Stella, A Themelis, P Patrinos
Computational Optimization and Applications 67 (3), 443–487, 2017
1372017
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
1212017
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
1192020
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
1182020
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
1092018
Gluonts: Probabilistic Time Series Models in Python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
arXiv preprint arXiv:1906.05264, 2019
1022019
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
882020
Douglas-Rachford splitting: Complexity estimates and accelerated variants
P Patrinos, L Stella, A Bemporad
53rd IEEE Conference on Decision and Control, 4234-4239, 2014
882014
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
712022
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
602013
Forward-backward truncated Newton methods for convex composite optimization
P Patrinos, L Stella, A Bemporad
arXiv preprint arXiv:1402.6655, 2014
442014
Proximal gradient algorithms: Applications in signal processing
N Antonello, L Stella, P Patrinos, T van Waterschoot
arXiv preprint arXiv:1803.01621, 2018
282018
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
202016
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
152022
Newton-type alternating minimization algorithm for convex optimization
L Stella, A Themelis, P Patrinos
IEEE Transactions on Automatic Control 64 (2), 697-711, 2019
142019
Now available in amazon sagemaker: Deepar algorithm for more accurate time series forecasting
T Januschowski, D Arpin, D Salinas, V Flunkert, J Gasthaus, L Stella, ...
AWS machine learning blog, 2018
142018
Anomaly detection at scale: The case for deep distributional time series models
F Ayed, L Stella, T Januschowski, J Gasthaus
International Conference on Service-Oriented Computing, 97-109, 2020
102020
Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient
P Latafat, A Themelis, L Stella, P Patrinos
arXiv preprint arXiv:2301.04431, 2023
72023
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