Syama Sundar Rangapuram
Syama Sundar Rangapuram
Amazon
Verified email at amazon.de
Title
Cited by
Cited by
Year
Deep state space models for time series forecasting
SS Rangapuram, MW Seeger, J Gasthaus, L Stella, Y Wang, ...
Advances in neural information processing systems 31, 7785-7794, 2018
1182018
Constrained 1-Spectral Clustering.
SS Rangapuram, M Hein
AISTATS 30, 90, 2012
962012
Towards realistic team formation in social networks based on densest subgraphs
SS Rangapuram, T Bühler, M Hein
Proceedings of the 22nd international conference on World Wide Web, 1077-1088, 2013
842013
The total variation on hypergraphs-learning on hypergraphs revisited
M Hein, S Setzer, L Jost, SS Rangapuram
Advances in Neural Information Processing Systems 26, 2427-2435, 2013
842013
Probabilistic forecasting with spline quantile function rnns
J Gasthaus, K Benidis, Y Wang, SS Rangapuram, D Salinas, V Flunkert, ...
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
302019
Gluonts: Probabilistic time series models in python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
arXiv preprint arXiv:1906.05264, 2019
242019
Constrained fractional set programs and their application in local clustering and community detection
T Bühler, SS Rangapuram, S Setzer, M Hein
International Conference on Machine Learning, 624-632, 2013
172013
Tight continuous relaxation of the balanced k-cut problem
SS Rangapuram, PK Mudrakarta, M Hein
Advances in Neural Information Processing Systems 27, 3131-3139, 2014
162014
Elastic Machine Learning Algorithms in Amazon SageMaker
E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ...
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
142020
Neural forecasting: Introduction and literature overview
K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ...
arXiv preprint arXiv:2004.10240, 2020
132020
Methods for sparse and low-rank recovery under simplex constraints
P Li, SS Rangapuram, M Slawski
arXiv preprint arXiv:1605.00507, 2016
112016
Approximate bayesian inference in linear state space models for intermittent demand forecasting at scale
M Seeger, S Rangapuram, Y Wang, D Salinas, J Gasthaus, ...
arXiv preprint arXiv:1709.07638, 2017
92017
Deep Learning for Forecasting: Current Trends and Challenges.
T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot
Foresight: The International Journal of Applied Forecasting, 2018
82018
GluonTS: Probabilistic Time Series Modeling in Python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
arXiv preprint arXiv:1906.05264, 2019
62019
GluonTS: Probabilistic and Neural Time Series Modeling in Python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
Journal of Machine Learning Research 21 (116), 1-6, 2020
42020
Deep learning for forecasting
T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot
Foresight: The International Journal of Applied Forecasting, 35-41, 2018
32018
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
R Kurle, SS Rangapuram, E de Bézenac, S Günnemann, J Gasthaus
Advances in Neural Information Processing Systems 33, 2020
2020
Normalizing Kalman Filters for Multivariate Time Series Analysis
E de Bézenac, SS Rangapuram, K Benidis, M Bohlke-Schneider, R Kurle, ...
Advances in Neural Information Processing Systems 33, 2020
2020
Graph-based methods for unsupervised and semi-supervised data analysis
SS Rangapuram
2016
Neural time series models with GluonTS Time Series Workshop ICML 2019
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
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