Infinite-Horizon Gaussian Processes
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A4 Artikkeli konferenssijulkaisussa
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Date
2018
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en
Pages
10
3490-3499
3490-3499
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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
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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 >