Sequential Gaussian Processes for Online Learning of Nonstationary Functions

24 May 2019Michael Minyi ZhangBianca DumitrascuSinead A. WilliamsonBarbara E. Engelhardt

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... (read more)

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