Infinite-Horizon Gaussian Processes

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
3490-3499
Series
32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada., Advances in Neural Information Processing Systems, Volume 31
Abstract
Gaussian processes provide a flexible framework for forecasting, removing noise, and interpreting long temporal datasets. State space modelling (Kalman filtering) enables these non-parametric models to be deployed on long datasets by reducing the complexity to linear in the number of data points. The complexity is still cubic in the state dimension m which is an impediment to practical application. In certain special cases (Gaussian likelihood, regular spacing) the GP posterior will reach a steady posterior state when the data are very long. We leverage this and formulate an inference scheme for GPs with general likelihoods, where inference is based on single-sweep EP (assumed density filtering). The infinite-horizon model tackles the cubic cost in the state dimensionality and reduces the cost in the state dimension m to O(m^2) per data point. The model is extended to online-learning of hyperparameters. We show examples for large finite-length modelling problems, and present how the method runs in real-time on a smartphone on a continuous data stream updated at 100 Hz.
Description
Keywords
Other note
Citation
Solin , A , Hensman , J & Turner , R E 2018 , Infinite-Horizon Gaussian Processes . in 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada. . Advances in Neural Information Processing Systems , vol. 31 , Curran Associates Inc. , pp. 3490-3499 , Conference on Neural Information Processing Systems , Montréal , Canada , 02/12/2018 . < http://papers.nips.cc/paper/7608-infinite-horizon-gaussian-processes.pdf >