Ab initio instanton rate theory made efficient using Gaussian process regression

Efficient Multiscale Gaussian Process Regression using Hierarchical Clustering

Standard Gaussian Process (GP) regression, a powerful machine learning tool, is computationally expensive when it is applied to large datasets, and potentially inaccurate when data points are sparsely distributed in a highdimensional feature space. To address these challenges, a new multiscale, sparsified GP algorithm is formulated, with the goal of application to large scientific computing dat...

Efficient Gaussian process regression for large datasets.

Gaussian processes are widely used in nonparametric regression, classification and spatiotemporal modelling, facilitated in part by a rich literature on their theoretical properties. However, one of their practical limitations is expensive computation, typically on the order of n3 where n is the number of data points, in performing the necessary matrix inversions. For large datasets, storage an...

Can Gaussian Process Regression Be Made Robust Against Model Mismatch?

Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), exhibiting e.g. multiple overfitting maxima and logarithmically slow learning. I investigate whether GPs can be made robust against such effects by adapting student model hyperparameters to maximize the evidence (data likelihoo...

Ab initio molecular orbital theory

Gaussian Process Quantile Regression using Expectation Propagation

Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagat...