Bidirectional relay communication with additional private message. - Konstantinos Benidis; Rafael F. Schaefer; Holger Boche (SPAWC, 2013)

Orthogonal sparse eigenvectors: A procrustes problem. - Konstantinos Benidis; Ying Sun 003; Prabhu Babu; Daniel Pérez Palomar (ICASSP, 2016)

Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation. - Konstantinos Benidis; Ying Sun 003; Prabhu Babu; Daniel P. Palomar (IEEE Trans. Signal Process., 2016)

Optimization Methods for Financial Index Tracking: From Theory to Practice. - Konstantinos Benidis; Yiyong Feng; Daniel P. Palomar (Found. Trends Optim., 2018)

Sparse Portfolios for High-Dimensional Financial Index Tracking. - Konstantinos Benidis; Yiyong Feng; Daniel P. Palomar (IEEE Trans. Signal Process., 2018)

Probabilistic Forecasting with Spline Quantile Function RNNs. - Jan Gasthaus; Konstantinos Benidis; Yuyang Wang 001; Syama Sundar Rangapuram; David Salinas; Valentin Flunkert; Tim Januschowski (AISTATS, 2019)

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)

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)

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)

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)

End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. - Syama Sundar Rangapuram; Lucien D. Werner; Konstantinos Benidis; Pedro Mercado; Jan Gasthaus; Tim Januschowski (ICML, 2021)

Deep Explicit Duration Switching Models for Time Series. - Abdul Fatir Ansari; Konstantinos Benidis; Richard Kurle; Ali Caner Türkmen; Harold Soh; Alexander J. Smola; Bernie Wang 001; Tim Januschowski (NeurIPS, 2021)

Multivariate Quantile Function Forecaster. - Kelvin Kan; François-Xavier Aubet; Tim Januschowski; Youngsuk Park; Konstantinos Benidis; Lars Ruthotto; Jan Gasthaus (AISTATS, 2022)

Solving Recurrent MIPs with Semi-supervised Graph Neural Networks. - Konstantinos Benidis; Ugo Rosolia; Syama Rangapuram; George Iosifidis; Georgios S. Paschos (CoRR, 2023)

Middle-mile optimization for next-day delivery. - Konstantinos Benidis; Georgios S. Paschos; Martin Groß 001; George Iosifidis (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)

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)