Guided Deep Kernel Learning

TL;DR

This paper introduces a novel deep kernel learning method guided by Neural Network Gaussian Processes, enhancing uncertainty estimation and robustness while maintaining scalability and accuracy across diverse datasets.

Contribution

The study proposes using NNGP as a guide for deep kernel learning, improving Bayesian properties and uncertainty estimation in deep kernel models.

Findings

Robustness to overfitting demonstrated on benchmark datasets

Achieves accurate uncertainty estimation

Maintains scalability and predictive performance

Abstract

Combining Gaussian processes with the expressive power of deep neural networks is commonly done nowadays through deep kernel learning (DKL). Unfortunately, due to the kernel optimization process, this often results in losing their Bayesian benefits. In this study, we present a novel approach for learning deep kernels by utilizing infinite-width neural networks. We propose to use the Neural Network Gaussian Process (NNGP) model as a guide to the DKL model in the optimization process. Our approach harnesses the reliable uncertainty estimation of the NNGPs to adapt the DKL target confidence when it encounters novel data points. As a result, we get the best of both worlds, we leverage the Bayesian behavior of the NNGP, namely its robustness to overfitting, and accurate uncertainty estimation, while maintaining the generalization abilities, scalability, and flexibility of deep kernels. Empirically, we show on multiple benchmark datasets of varying sizes and dimensionality, that our method is robust to overfitting, has good predictive performance, and provides reliable uncertainty estimations.