CUED Publications database

Streaming sparse Gaussian process approximations

Bui, TD and Nguyen, CV and Turner, RE (2017) Streaming sparse Gaussian process approximations. In: UNSPECIFIED pp. 3300-3308..

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: stat.ML stat.ML
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 03 Aug 2017 02:37
Last Modified: 10 Apr 2021 22:26