Kernel Identification Through Transformers

TL;DR

This paper introduces KITT, a transformer-based method for rapid kernel selection in Gaussian Process models, significantly speeding up the process while maintaining strong performance across various regression tasks.

Contribution

KITT is a novel transformer-based approach that efficiently recommends kernels for high-dimensional GP regression, outperforming traditional search methods in speed.

Findings

KITT generates kernel recommendations in under 0.1 seconds.

Kernels suggested by KITT perform well on diverse regression benchmarks.

KITT effectively handles datasets with inputs of arbitrary dimension.

Abstract

Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of functions under the GP prior. This work addresses the challenge of constructing custom kernel functions for high-dimensional GP regression models. Drawing inspiration from recent progress in deep learning, we introduce a novel approach named KITT: Kernel Identification Through Transformers. KITT exploits a transformer-based architecture to generate kernel recommendations in under 0.1 seconds, which is several orders of magnitude faster than conventional kernel search algorithms. We train our model using synthetic data generated from priors over a vocabulary of known kernels. By exploiting the nature of the self-attention mechanism, KITT is able to process datasets with inputs of arbitrary dimension. We demonstrate that kernels chosen by KITT yield strong performance over a diverse collection of regression benchmarks.