Lorenzo Stella

Recent publications (more in my Google Scholar profile):

  1. Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang, Chronos: Learning the Language of Time Series. Transactions on Machine Learning Research, online, 2024.

  2. Puya Latafat, Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos, Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient. Mathematical Programming, 10.1007/s10107-024-02143-7, 2024.

  3. Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos, Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type algorithms. Computational Optimization and Applications 82, pp. 395–440, 10.1007/s10589-022-00366-y, 2022.

  4. Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski. Normalizing Kalman Filters for Multivariate Time Series Analysis. Advances in Neural Information Processing Systems 33 (NeurIPS 2020), online, 2020.

  5. Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang. GluonTS: Probabilistic and Neural Time Series Modeling in Python, Journal of Machine Learning Research, Volume 21, online, 2020.

  6. Lorenzo Stella, Andreas Themelis, Panagiotis Patrinos. Newton-type alternating minimization algorithm for convex optimization. IEEE Transactions on Automatic Control, Volume 64, Issue 2, pp. 697-711, 10.1109/TAC.2018.2872203, 2019.

  7. Syama Rangapuram, Matthias Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski. Deep State Space Models for Time Series Forecasting. Advances in Neural Information Processing Systems 31 (NIPS 2018), online, 2018.

  8. Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos. Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone line-search algorithms. SIAM Journal on Optimization, Volume 28, Issue 3, pp. 2274–2303, 10.1137/16M1080240, 2018.

  9. Lorenzo Stella, Andreas Themelis, Panagiotis Patrinos. Forward-backward quasi-Newton methods for nonsmooth optimization problems. Computational Optimization and Applications, Volume 67, Issue 3, pp. 443–487, 10.1007/s10589-017-9912-y, 2017.

  10. Lorenzo Stella, Andreas Themelis, Pantelis Sopasakis, Panagiotis Patrinos. A simple and efficient algorithm for nonlinear model predictive control. 56th IEEE Conference on Decision and Control, 10.1109/CDC.2017.8263933, 2017.

  11. Puya Latafat, Lorenzo Stella, Panagiotis Patrinos. New primal-dual proximal algorithms for distributed optimization. 55th IEEE Conference on Decision and Control, pp. 1959-1964, 10.1109/CDC.2016.7798551, 2016.

  12. Panagiotis Patrinos, Lorenzo Stella, Alberto Bemporad. Douglas-Rachford splitting: complexity estimates and accelerated variants. 53rd IEEE Conference on Decision and Control, pp. 4234-4239, 10.1109/CDC.2014.7040049, 2014.

  13. Panagiotis Patrinos, Lorenzo Stella, Alberto Bemporad. Forward-backward truncated Newton methods for convex composite optimization. arXiv:1402.6655, 2014.