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We introduce a kernel approximation strategy that enables computation of the Gaussian process log marginal likelihood and all hyperparameter derivatives in $\mathcal{O}(p)$ time.
The numerical treatment of integral equations
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Radial basis functions: theory and implementations
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A unifying view of sparse approximate Gaussian process regression
Quiñonero-Candela, J. and Rasmussen, C. E · 2005
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Gaussian Processes for Machine Learning
Rasmussen, C. E. and Williams, C. K. I · 2006
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Rahimi, A. and Recht, B · 2007
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Zhang, K., Tsang, I. W., and Kwok, J. T · 2008
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Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling
Wang, S. and Zhang, Z · 2013
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PredictiveScience lab: py-mcmc
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Blitzkriging: Kronecker-structured stochastic Gaussian processes
Nickson, T., Gunter, T., Lloyd, C., Osborne, M. A., and Roberts, S · 2015
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EigenGP: Gaussian process models with adaptive eigenfunctions
Peng, H. and Qi, Y · 2015
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Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
Wilson, A. G. and Nickisch, H · 2015
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