Streaming Sparse Gaussian Process Approximations

2017-05-19 · NeurIPS 2017 12 · Thang D. Bui, Cuong V. Nguyen, Richard E. Turner

Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the hyperparameter estimates are updated in an online fashion. The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive. This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing methods for learning hyperparameters and optimising pseudo-input locations. The proposed framework is assessed using synthetic and real-world datasets.

📄 PDF Abstract BibTeX arXiv:1705.07131

Code (3)

thangbui/streaming_sparse_gp 공식 구현 tf

tyliu22/online_pacgp pytorch

wjmaddox/online_gp pytorch

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