On the optimization of hyperparameters in Gaussian process regression. - Sergei Manzhos; Manabu Ihara (CoRR, 2021)

Rectangularization of Gaussian process regression for optimization of hyperparameters. - Sergei Manzhos; Manabu Ihara (CoRR, 2021)

The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces. - Sergei Manzhos; Manabu Ihara (CoRR, 2022)

Random 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. - Owen Ren; Mohamed Ali Boussaidi; Dmitry A. Voytsekhovsky; Manabu Ihara; Sergei Manzhos (Comput. Phys. Commun., 2022)

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. - Sergei Manzhos; Eita Sasaki; Manabu Ihara (Mach. Learn. Sci. Technol., 2022)

Neural network with optimal neuron activation functions based on additive Gaussian process regression. - Sergei Manzhos; Manabu Ihara (CoRR, 2023)

Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization. - Sergei Manzhos; Manabu Ihara (CoRR, 2023)

Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension. - Sergei Manzhos; Manabu Ihara (CoRR, 2023)

Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory. - Sergei Manzhos; Johann Lüder; Pavlo Golub; Manabu Ihara (Mach. Learn. Sci. Technol., 2025)