Syama Sundar Rangapuram

Orcid: 0000-0002-9357-0154

According to our database1, Syama Sundar Rangapuram authored at least 25 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
A Flexible Forecasting Stack.
Proc. VLDB Endow., August, 2024

Chronos: Learning the Language of Time Series.
CoRR, 2024

2023
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.
ACM Comput. Surv., 2023

Deep Non-Parametric Time Series Forecaster.
CoRR, 2023

Adaptive Sampling for Probabilistic Forecasting under Distribution Shift.
CoRR, 2023

Coherent Probabilistic Forecasting of Temporal Hierarchies.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Multivariate Time Series Forecasting with Latent Graph Inference.
CoRR, 2022

2021
Neural Flows: Efficient Alternative to Neural ODEs.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
GluonTS: Probabilistic and Neural Time Series Modeling in Python.
J. Mach. Learn. Res., 2020

Neural forecasting: Introduction and literature overview.
CoRR, 2020


Deep Rao-Blackwellised Particle Filters for Time Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Normalizing Kalman Filters for Multivariate Time Series Analysis.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
GluonTS: Probabilistic Time Series Models in Python.
CoRR, 2019

Probabilistic Forecasting with Spline Quantile Function RNNs.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Deep State Space Models for Time Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale.
CoRR, 2017

2016
Graph-based methods for unsupervised and semi-supervised data analysis.
PhD thesis, 2016

Methods for Sparse and Low-Rank Recovery under Simplex Constraints.
CoRR, 2016

2014
Tight Continuous Relaxation of the Balanced k-Cut Problem.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Towards realistic team formation in social networks based on densest subgraphs.
Proceedings of the 22nd International World Wide Web Conference, 2013

The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Constrained fractional set programs and their application in local clustering and community detection.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Constrained 1-Spectral Clustering.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012


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