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Sequential Gaussian Processes for Online Learning of Nonstationary Functions
[article]
2023
arXiv
pre-print
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive. Gaussian processes (GPs) are an attractive choice for modeling real-valued nonlinear functions due to their flexibility and uncertainty quantification. However, the typical GP regression model suffers from several drawbacks: 1) Conventional GP inference scales O(N^3) with respect to the number of observations;
arXiv:1905.10003v4
fatcat:247z4yqk5zchfchkpiwotj26ja