Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces

TL;DR

This paper demonstrates that spectral regularization of models over combinatorial spaces improves generalization in data-scarce settings, supported by theoretical analysis and empirical validation on real-world problems.

Contribution

It provides a theoretical framework explaining how spectral regularization enables data-efficient learning over combinatorial spaces, with empirical evidence showing improved performance.

Findings

Spectral regularization reshapes the loss landscape for better generalization.

Stationary points that interpolate data achieve optimal generalization.

Gradient descent on regularized loss outperforms baselines in real-world tasks.

Abstract

Data-driven machine learning models are being increasingly employed in several important inference problems in biology, chemistry, and physics which require learning over combinatorial spaces. Recent empirical evidence (see, e.g., [1], [2], [3]) suggests that regularizing the spectral representation of such models improves their generalization power when labeled data is scarce. However, despite these empirical studies, the theoretical underpinning of when and how spectral regularization enables improved generalization is poorly understood. In this paper, we focus on learning pseudo-Boolean functions and demonstrate that regularizing the empirical mean squared error by the L_1 norm of the spectral transform of the learned function reshapes the loss landscape and allows for data-frugal learning, under a restricted secant condition on the learner's empirical error measured against the ground truth function. Under a weaker quadratic growth condition, we show that stationary points which also approximately interpolate the training data points achieve statistically optimal generalization performance. Complementing our theory, we empirically demonstrate that running gradient descent on the regularized loss results in a better generalization performance compared to baseline algorithms in several data-scarce real-world problems.