A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models

@inproceedings{Hoang2016ADV,
  title={A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models},
  author={Trong Nghia Hoang and Quang Minh Hoang and Kian Hsiang Low},
  booktitle={ICML},
  year={2016}
}
This paper presents a novel distributed variational inference framework that unifies many parallel sparse Gaussian process regression (SGPR) models for scalable hyperparameter learning with big data. To achieve this, our framework exploits a structure of correlated noise process model that represents the observation noises as a finite realization of a high-order Gaussian Markov random process. By varying the Markov order and covariance function for the noise process model, different variational… CONTINUE READING
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