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20202023
most citedRandom Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method

36 citations · 151 across the 8 of their papers we have counts for

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math.NA2021★ 18 cited

Rectangularization of Gaussian process regression for optimization of hyperparameters

Sergei Manzhos, Manabu Ihara

Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR u…

math.NA2021★ 21 cited

Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

Eita Sasaki, Manabu Ihara, Sergei Manzhos

We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-di…