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ORCIDSyama Sundar Rangapuram
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2020 – today
- 2024
[j3]Tim Januschowski, Yuyang Wang, Jan Gasthaus, Syama Sundar Rangapuram, Caner Turkmen, Jasper Zschiegner, Lorenzo Stella, Michael Bohlke-Schneider, Danielle C. Maddix, Konstantinos Benidis, Alexander Alexandrov, Christos Faloutsos, Sebastian Schelter:
A Flexible Forecasting Stack. Proc. VLDB Endow. 17(12): 3883-3892 (2024)- [i14]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:
Chronos: Learning the Language of Time Series. CoRR abs/2403.07815 (2024) - 2023
- [j2]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:
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv. 55(6): 121:1-121:36 (2023) - [c13]Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang, Michael Bohlke-Schneider:
Coherent Probabilistic Forecasting of Temporal Hierarchies. AISTATS 2023: 9362-9376 - [i13]Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider:
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift. CoRR abs/2302.11870 (2023) - [i12]Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski:
Deep Non-Parametric Time Series Forecaster. CoRR abs/2312.14657 (2023) - 2022
- [i11]Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski:
Multivariate Time Series Forecasting with Latent Graph Inference. CoRR abs/2203.03423 (2022) - 2021
- [c12]Syama Sundar Rangapuram, Lucien D. Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski:
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. ICML 2021: 8832-8843 - [c11]Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann:
Neural Flows: Efficient Alternative to Neural ODEs. NeurIPS 2021: 21325-21337 - [i10]Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann:
Neural Flows: Efficient Alternative to Neural ODEs. CoRR abs/2110.13040 (2021) - 2020
- [j1]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:
GluonTS: Probabilistic and Neural Time Series Modeling in Python. J. Mach. Learn. Res. 21: 116:1-116:6 (2020) - [c10]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. NeurIPS 2020 - [c9]Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus:
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS 2020 - [c8]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:
Elastic Machine Learning Algorithms in Amazon SageMaker. SIGMOD Conference 2020: 731-737 - [i9]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:
Neural forecasting: Introduction and literature overview. CoRR abs/2004.10240 (2020)
2010 – 2019
- 2019
- [c7]Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski:
Probabilistic Forecasting with Spline Quantile Function RNNs. AISTATS 2019: 1901-1910 - [i8]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:
GluonTS: Probabilistic Time Series Models in Python. CoRR abs/1906.05264 (2019) - 2018
- [c6]Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski:
Deep State Space Models for Time Series Forecasting. NeurIPS 2018: 7796-7805 - 2017
- [i7]Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert:
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. CoRR abs/1709.07638 (2017) - 2016
- [b1]Syama Sundar Rangapuram:
Graph-based methods for unsupervised and semi-supervised data analysis. Saarland University, Saarbrücken, Germany, 2016 - [i6]Ping Li, Syama Sundar Rangapuram, Martin Slawski:
Methods for Sparse and Low-Rank Recovery under Simplex Constraints. CoRR abs/1605.00507 (2016) - 2015
- [i5]Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein:
Tight Continuous Relaxation of the Balanced k-Cut Problem. CoRR abs/1505.06478 (2015) - [i4]Syama Sundar Rangapuram, Matthias Hein:
Constrained 1-Spectral Clustering. CoRR abs/1505.06485 (2015) - [i3]Syama Sundar Rangapuram, Thomas Bühler, Matthias Hein:
Towards Realistic Team Formation in Social Networks based on Densest Subgraphs. CoRR abs/1505.06661 (2015) - 2014
- [c5]Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein:
Tight Continuous Relaxation of the Balanced k-Cut Problem. NIPS 2014: 3131-3139 - 2013
- [c4]Thomas Bühler, Syama Sundar Rangapuram, Simon Setzer, Matthias Hein:
Constrained fractional set programs and their application in local clustering and community detection. ICML (1) 2013: 624-632 - [c3]Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram:
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited. NIPS 2013: 2427-2435 - [c2]Syama Sundar Rangapuram, Thomas Bühler, Matthias Hein:
Towards realistic team formation in social networks based on densest subgraphs. WWW 2013: 1077-1088 - [i2]Thomas Bühler, Syama Sundar Rangapuram, Simon Setzer, Matthias Hein:
Constrained fractional set programs and their application in local clustering and community detection. CoRR abs/1306.3409 (2013) - [i1]Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram:
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited. CoRR abs/1312.5179 (2013) - 2012
- [c1]Syama Sundar Rangapuram, Matthias Hein:
Constrained 1-Spectral Clustering. AISTATS 2012: 1143-1151
Coauthor Index
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