Fast Gaussian Process Approximations for Autocorrelated Data

A recent study introduces a multi-view Bayesian optimisation approach that enhances the efficiency of Gaussian process models in engineering design by identifying a low-dimensional latent subspace from input and output data. This method utilizes probabilistic partial least squares (PPLS) to improve the scalability of Bayesian optimisation techniques in complex design scenarios.

Recent advancements in Gaussian process (GP) regression have led to the development of an adaptive pruning strategy aimed at enhancing the robustness and efficiency of saddle point searches in high-dimensional energy landscapes. This approach utilizes geometry-aware optimal transport measures and a permutation-invariant metric to optimize computational overhead and improve stability during hyperparameter optimization.

A new study introduces a Bayesian tensor train kernel machine that utilizes Laplace approximation to enhance Gaussian process regression in system identification. This method addresses scalability issues by estimating the posterior distribution over a selected tensor train core while employing variational inference for hyperparameter precision. Experiments indicate that core selection is largely independent of tensor train ranks and feature structures, achieving significantly faster training times.