Adaptive Inducing Points Selection For Gaussian Processes

TL;DR

This paper introduces an adaptive method for selecting inducing points in Gaussian Processes, enhancing online inference by dynamically adjusting the points based on data and model properties.

Contribution

It proposes a novel approach to adaptively add inducing points in Gaussian Processes, improving streaming data inference beyond existing static methods.

Findings

Improved online GP performance with adaptive inducing points.

Enhanced data representation through dynamic point addition.

Better model accuracy in streaming scenarios.

Abstract

Gaussian Processes (\textbf{GPs}) are flexible non-parametric models with strong probabilistic interpretation. While being a standard choice for performing inference on time series, GPs have few techniques to work in a streaming setting. \cite{bui2017streaming} developed an efficient variational approach to train online GPs by using sparsity techniques: The whole set of observations is approximated by a smaller set of inducing points (\textbf{IPs}) and moved around with new data. Both the number and the locations of the IPs will affect greatly the performance of the algorithm. In addition to optimizing their locations, we propose to adaptively add new points, based on the properties of the GP and the structure of the data.