Constrained 1-Spectral Clustering. - Syama Sundar Rangapuram; Matthias Hein 001 (AISTATS, 2012)

Constrained fractional set programs and their application in local clustering and community detection. - Thomas Bühler; Syama Sundar Rangapuram; Simon Setzer; Matthias Hein 001 (ICML (1), 2013)

The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited. - Matthias Hein 001; Simon Setzer; Leonardo Jost; Syama Sundar Rangapuram (NIPS, 2013)

Towards realistic team formation in social networks based on densest subgraphs. - Syama Sundar Rangapuram; Thomas Bühler; Matthias Hein 001 (WWW, 2013)

Tight Continuous Relaxation of the Balanced k-Cut Problem. - Syama Sundar Rangapuram; Pramod Kaushik Mudrakarta; Matthias Hein 001 (NIPS, 2014)

Methods for Sparse and Low-Rank Recovery under Simplex Constraints. - Ping Li 001; Syama Sundar Rangapuram; Martin Slawski (CoRR, 2016)

Graph-based methods for unsupervised and semi-supervised data analysis. - Syama Sundar Rangapuram (2016)

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. - Matthias W. Seeger; Syama Sundar Rangapuram; Yuyang Wang 001; David Salinas; Jan Gasthaus; Tim Januschowski; Valentin Flunkert (CoRR, 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)

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)

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. - Richard Kurle; Syama Sundar Rangapuram; Emmanuel de Bézenac; Stephan Günnemann; Jan Gasthaus (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)

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)

Neural Flows: Efficient Alternative to Neural ODEs. - Marin Bilos; Johanna Sommer; Syama Sundar Rangapuram; Tim Januschowski; Stephan Günnemann (NeurIPS, 2021)

Multivariate Time Series Forecasting with Latent Graph Inference. - Victor Garcia Satorras; Syama Sundar Rangapuram; Tim Januschowski (CoRR, 2022)

Coherent Probabilistic Forecasting of Temporal Hierarchies. - Syama Sundar Rangapuram; Shubham Kapoor; Rajbir-Singh Nirwan; Pedro Mercado; Tim Januschowski; Yuyang Wang 001; Michael Bohlke-Schneider (AISTATS, 2023)

Adaptive Sampling for Probabilistic Forecasting under Distribution Shift. - Luca Masserano; Syama Sundar Rangapuram; Shubham Kapoor; Rajbir-Singh Nirwan; Youngsuk Park; Michael Bohlke-Schneider (CoRR, 2023)

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)

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. - Luca Masserano; Abdul Fatir Ansari; Boran Han; Xiyuan Zhang; Christos Faloutsos; Michael W. Mahoney; Andrew Gordon Wilson; Youngsuk Park; Syama Sundar Rangapuram; Danielle C. Maddix; Yuyang Wang 001 (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)