Syama Sundar Rangapuram

38 papers A* 7A 4Journal 22Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Chronos-2: From Univariate to Universal Forecasting. CoRR Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, Andreas Auer, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyuan Zhang, Mononito Goswami, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, Michael Bohlke-Schneider
2025	A	`conf`	ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. AISTATS 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, Syama Sundar Rangapuram
2025	J	`jnl`	ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. CoRR 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, Syama Sundar Rangapuram
2025	A*	`conf`	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. ICML 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, Bernie Wang
2025	J	`jnl`	Zero-Shot Time Series Forecasting with Covariates via In-Context Learning. CoRR Andreas Auer, Raghul Parthipan, Pedro Mercado, Abdul Fatir Ansari, Lorenzo Stella, Yuyang Wang, Michael Bohlke-Schneider, Syama Sundar Rangapuram
2024	J	`jnl`	A Flexible Forecasting Stack. Proc. VLDB Endow. Tim Januschowski, Yuyang Wang, Jan Gasthaus, Syama Sundar Rangapuram, Caner Türkmen, Jasper Zschiegner, Lorenzo Stella, Michael Bohlke-Schneider, Danielle C. Maddix, Konstantinos Benidis, Alexander Alexandrov, Christos Faloutsos, Sebastian Schelter
2024	J	`jnl`	Chronos: Learning the Language of Time Series. CoRR 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, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
2024	J	`jnl`	Chronos: Learning the Language of Time Series. Trans. Mach. Learn. Res. 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, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Bernie Wang
2024	J	`jnl`	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. CoRR 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
2023	J	`jnl`	Adaptive Sampling for Probabilistic Forecasting under Distribution Shift. CoRR Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider
2023	A	`conf`	Coherent Probabilistic Forecasting of Temporal Hierarchies. AISTATS Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang, Michael Bohlke-Schneider
2023	J	`jnl`	Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv. Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot, Tim Januschowski
2023	J	`jnl`	Deep Non-Parametric Time Series Forecaster. CoRR Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
2022	J	`jnl`	Multivariate Time Series Forecasting with Latent Graph Inference. CoRR Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski
2021	A*	`conf`	End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. ICML Syama Sundar Rangapuram, Lucien D. Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski
2021	A*	`conf`	Neural Flows: Efficient Alternative to Neural ODEs. NeurIPS Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
2021	J	`jnl`	Neural Flows: Efficient Alternative to Neural ODEs. CoRR Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
2020	A*	`conf`	Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
2020	—	`conf`	Elastic Machine Learning Algorithms in Amazon SageMaker. SIGMOD Conference 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, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Sundar Rangapuram, David Salinas, Sebastian Schelter, Alex Smola
2020	J	`jnl`	GluonTS: Probabilistic and Neural Time Series Modeling in Python. J. Mach. Learn. Res. Alexander Alexandrov, 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, Yuyang Wang
2020	J	`jnl`	Neural forecasting: Introduction and literature overview. CoRR Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski
2020	A*	`conf`	Normalizing Kalman Filters for Multivariate Time Series Analysis. NeurIPS Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski
2019	J	`jnl`	GluonTS: Probabilistic Time Series Models in Python. CoRR Alexander Alexandrov, 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, Yuyang Wang
2019	A	`conf`	Probabilistic Forecasting with Spline Quantile Function RNNs. AISTATS Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski
2018	A*	`conf`	Deep State Space Models for Time Series Forecasting. NeurIPS Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
2017	J	`jnl`	Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. CoRR Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
2016	—	`—`	Graph-based methods for unsupervised and semi-supervised data analysis. Syama Sundar Rangapuram
2016	J	`jnl`	Methods for Sparse and Low-Rank Recovery under Simplex Constraints. CoRR Ping Li, Syama Sundar Rangapuram, Martin Slawski
2015	J	`jnl`	Constrained 1-Spectral Clustering. CoRR Syama Sundar Rangapuram, Matthias Hein
2015	J	`jnl`	Tight Continuous Relaxation of the Balanced k-Cut Problem. CoRR Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
2015	J	`jnl`	Towards Realistic Team Formation in Social Networks based on Densest Subgraphs. CoRR Syama Sundar Rangapuram, Thomas Bühler, Matthias Hein
2014	—	`conf`	Tight Continuous Relaxation of the Balanced k-Cut Problem. NIPS Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
2013	—	`conf`	Constrained fractional set programs and their application in local clustering and community detection. ICML (1) Thomas Bühler, Syama Sundar Rangapuram, Simon Setzer, Matthias Hein
2013	J	`jnl`	Constrained fractional set programs and their application in local clustering and community detection. CoRR Thomas Bühler, Syama Sundar Rangapuram, Simon Setzer, Matthias Hein
2013	—	`conf`	The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited. NIPS Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram
2013	J	`jnl`	The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited. CoRR Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram
2013	A*	`conf`	Towards realistic team formation in social networks based on densest subgraphs. WWW Syama Sundar Rangapuram, Thomas Bühler, Matthias Hein
2012	A	`conf`	Constrained 1-Spectral Clustering. AISTATS Syama Sundar Rangapuram, Matthias Hein