An adaptive transfer learning perspective on classification in non-stationary environments

TL;DR

This paper introduces a statistical adaptive transfer learning approach for semi-supervised classification under non-stationary label-shift, providing regret bounds that adapt to changing label distributions over time.

Contribution

It proposes a novel adaptive transfer learning method with theoretical regret bounds for non-stationary environments, outperforming traditional online gradient descent variants.

Findings

High-probability regret bounds established

Method adapts automatically to unknown label dynamics

Bounds match online learning guarantees over time

Abstract

We consider a semi-supervised classification problem with non-stationary label-shift in which we observe a labelled data set followed by a sequence of unlabelled covariate vectors in which the marginal probabilities of the class labels may change over time. Our objective is to predict the corresponding class-label for each covariate vector, without ever observing the ground-truth labels, beyond the initial labelled data set. Previous work has demonstrated the potential of sophisticated variants of online gradient descent to perform competitively with the optimal dynamic strategy (Bai et al. 2022). In this work we explore an alternative approach grounded in statistical methods for adaptive transfer learning. We demonstrate the merits of this alternative methodology by establishing a high-probability regret bound on the test error at any given individual test-time, which adapt automatically to the unknown dynamics of the marginal label probabilities. Further more, we give bounds on the average dynamic regret which match the average guarantees of the online learning perspective for any given time interval.