Batch Active Learning in Gaussian Process Regression using Derivatives

TL;DR

This paper introduces a novel batch active learning method for Gaussian Process regression that leverages derivative information and predictive covariance to select informative data samples, demonstrating improved performance across various applications.

Contribution

The paper presents a new algorithm that uses derivatives and covariance for batch selection in Gaussian Process regression, with theoretical analysis and empirical validation.

Findings

Enhanced sample selection using derivatives improves model accuracy.

The proposed method outperforms traditional active learning approaches.

Empirical results confirm the approach's effectiveness across multiple datasets.

Abstract

We investigate the use of derivative information for Batch Active Learning in Gaussian Process regression models. The proposed approach employs the predictive covariance matrix for selection of data batches to exploit full correlation of samples. We theoretically analyse our proposed algorithm taking different optimality criteria into consideration and provide empirical comparisons highlighting the advantage of incorporating derivatives information. Our results show the effectiveness of our approach across diverse applications.