Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations

9 Aug 2014Jie ChenNannan CaoKian Hsiang LowRuofei OuyangColin Keng-Yan TanPatrick Jaillet

Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size... (read more)

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