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
1622018
The total variation on hypergraphs-learning on hypergraphs revisited
M Hein, S Setzer, L Jost, SS Rangapuram
arXiv preprint arXiv:1312.5179, 2013
1002013
Constrained 1-spectral clustering
SS Rangapuram, M Hein
Artificial Intelligence and Statistics, 1143-1151, 2012
992012
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
902013
Neural forecasting: Introduction and literature overview
K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ...
arXiv preprint arXiv:2004.10240, 2020
382020
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
382019
Gluonts: Probabilistic time series models in python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
arXiv preprint arXiv:1906.05264, 2019
352019
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
302020
GluonTS: Probabilistic and Neural Time Series Modeling in Python.
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
J. Mach. Learn. Res. 21 (116), 1-6, 2020
242020
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
182013
Tight Continuous Relaxation of the Balanced k-Cut Problem.
SS Rangapuram, PK Mudrakarta, M Hein
NIPS, 3131-3139, 2014
132014
Methods for sparse and low-rank recovery under simplex constraints
P Li, SS Rangapuram, M Slawski
arXiv preprint arXiv:1605.00507, 2016
122016
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
102017
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
92018
GluonTS: Probabilistic Time Series Modeling in Python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
arXiv preprint arXiv:1906.05264, 2019
82019
Normalizing Kalman Filters for Multivariate Time Series Analysis.
E de Bézenac, SS Rangapuram, K Benidis, M Bohlke-Schneider, R Kurle, ...
NeurIPS, 2020
72020
Deep learning for forecasting
T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot
Foresight: The International Journal of Applied Forecasting, 35-41, 2018
52018
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
32020
Methods for Sparse and Low-Rank Recovery under Simplex Constraints
P Li, SS Rangapuram, M Slawski
Statistica Sinica 30 (2), 557-577, 2020
12020
Tight Continuous Relaxation of the Balanced -Cut Problem
SS Rangapuram, PK Mudrakarta, M Hein
arXiv preprint arXiv:1505.06478, 2015
12015
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Articles 1–20