Douglas-rachford splitting: Complexity estimates and accelerated variants. - Panagiotis Patrinos; Lorenzo Stella; Alberto Bemporad (CDC, 2014)

New primal-dual proximal algorithm for distributed optimization. - Puya Latafat; Lorenzo Stella; Panagiotis Patrinos (CDC, 2016)

A simple and efficient algorithm for nonlinear model predictive control. - Lorenzo Stella; Andreas Themelis; Pantelis Sopasakis; Panagiotis Patrinos (CDC, 2017)

Forward-backward quasi-Newton methods for nonsmooth optimization problems. - Lorenzo Stella; Andreas Themelis; Panagiotis Patrinos (Comput. Optim. Appl., 2017)

Deep State Space Models for Time Series Forecasting. - Syama Sundar Rangapuram; Matthias W. Seeger; Jan Gasthaus; Lorenzo Stella; Yuyang Wang 001; Tim Januschowski (NeurIPS, 2018)

Discriminating between Different Heavy Metal Ions with Fullerene-Derived Nanoparticles. - Erica Ciotta; Paolo Prosposito; Pietro Tagliatesta; Chiara Lorecchio; Lorenzo Stella; Saulius Kaciulis; Peiman Soltani; Ernesto Placidi; Roberto Pizzoferrato (Sensors, 2018)

Forward-Backward Envelope for the Sum of Two Nonconvex Functions: Further Properties and Nonmonotone Linesearch Algorithms. - Andreas Themelis; Lorenzo Stella; Panagiotis Patrinos (SIAM J. Optim., 2018)

GluonTS: Probabilistic Time Series Models in Python. - Alexander Alexandrov 001; Konstantinos Benidis; Michael Bohlke-Schneider; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Danielle C. Maddix; Syama Sundar Rangapuram; David Salinas; Jasper Schulz; Lorenzo Stella; Ali Caner Türkmen; et al. (CoRR, 2019)

Newton-Type Alternating Minimization Algorithm for Convex Optimization. - Lorenzo Stella; Andreas Themelis; Panagiotis Patrinos (IEEE Trans. Autom. Control., 2019)

Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models. - Fadhel Ayed; Lorenzo Stella; Tim Januschowski; Jan Gasthaus (ICSOC Workshops, 2020)

Normalizing Kalman Filters for Multivariate Time Series Analysis. - Emmanuel de Bézenac; Syama Sundar Rangapuram; Konstantinos Benidis; Michael Bohlke-Schneider; Richard Kurle; Lorenzo Stella; Hilaf Hasson; Patrick Gallinari; Tim Januschowski (NeurIPS, 2020)

Elastic Machine Learning Algorithms in Amazon SageMaker. - Edo Liberty; Zohar S. Karnin; Bing Xiang; Laurence Rouesnel; Baris Coskun; Ramesh Nallapati; Julio Delgado; Amir Sadoughi; Yury Astashonok; Piali Das; Can Balioglu; Saswata Chakravarty; et al. (SIGMOD Conference, 2020)

Neural forecasting: Introduction and literature overview. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Bernie Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; Laurent Callot; Tim Januschowski (CoRR, 2020)

Structural Determinants of Phosphopeptide Binding to the N-Terminal Src Homology 2 Domain of the SHP2 Phosphatase. - Massimiliano Anselmi; Paolo Calligari; Jochen S. Hub; Marco Tartaglia; Gianfranco Bocchinfuso; Lorenzo Stella (J. Chem. Inf. Model., 2020)

GluonTS: Probabilistic and Neural Time Series Modeling in Python. - Alexander Alexandrov 001; Konstantinos Benidis; Michael Bohlke-Schneider; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Danielle C. Maddix; Syama Sundar Rangapuram; David Salinas; Jasper Schulz; Lorenzo Stella; Ali Caner Türkmen; et al. (J. Mach. Learn. Res., 2020)

Douglas-Rachford splitting and ADMM for nonconvex optimization: accelerated and Newton-type linesearch algorithms. - Andreas Themelis; Lorenzo Stella; Panagiotis Patrinos (Comput. Optim. Appl., 2022)

Deep Non-Parametric Time Series Forecaster. - Syama Sundar Rangapuram; Jan Gasthaus; Lorenzo Stella; Valentin Flunkert; David Salinas; Yuyang Wang 001; Tim Januschowski (CoRR, 2023)

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Yuyang Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; François-Xavier Aubet; Laurent Callot; et al. (ACM Comput. Surv., 2023)

Chronos: Learning the Language of Time Series. - Abdul Fatir Ansari; Lorenzo Stella; Ali Caner Türkmen; Xiyuan Zhang; Pedro Mercado; Huibin Shen; Oleksandr Shchur; Syama Sundar Rangapuram; Sebastian Pineda-Arango; Shubham Kapoor; Jasper Zschiegner; Danielle C. Maddix; et al. (CoRR, 2024)

Chronos: Learning the Language of Time Series. - Abdul Fatir Ansari; Lorenzo Stella; Ali Caner Türkmen; Xiyuan Zhang; Pedro Mercado; Huibin Shen; Oleksandr Shchur; Syama Sundar Rangapuram; Sebastian Pineda-Arango; Shubham Kapoor; Jasper Zschiegner; Danielle C. Maddix; et al. (Trans. Mach. Learn. Res., 2024)

A Flexible Forecasting Stack. - Tim Januschowski; Yuyang Wang 001; Jan Gasthaus; Syama Sundar Rangapuram; Caner Türkmen; Jasper Zschiegner; Lorenzo Stella; Michael Bohlke-Schneider; Danielle C. Maddix; Konstantinos Benidis; Alexander Alexandrov 001; Christos Faloutsos; et al. (Proc. VLDB Endow., 2024)

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. - Sebastian Pineda Arango; Pedro Mercado; Shubham Kapoor; Abdul Fatir Ansari; Lorenzo Stella; Huibin Shen; Hugo Senetaire; Ali Caner Türkmen; Oleksandr Shchur; Danielle C. Maddix; Michael Bohlke-Schneider; Bernie Wang 001; et al. (AISTATS, 2025)

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. - Sebastian Pineda-Arango; Pedro Mercado; Shubham Kapoor; Abdul Fatir Ansari; Lorenzo Stella; Huibin Shen; Hugo Senetaire; Caner Turkmen; Oleksandr Shchur; Danielle C. Maddix; Michael Bohlke-Schneider; Yuyang Wang 001; et al. (CoRR, 2025)

Zero-Shot Time Series Forecasting with Covariates via In-Context Learning. - Andreas Auer; Raghul Parthipan; Pedro Mercado; Abdul Fatir Ansari; Lorenzo Stella; Yuyang Wang 001; Michael Bohlke-Schneider; Syama Sundar Rangapuram (CoRR, 2025)

Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient. - Puya Latafat; Andreas Themelis; Lorenzo Stella; Panagiotis Patrinos (Math. Program., 2025)