@inproceedings{
wu2024online,
title={Online Feature Updates Improve Online (Generalized) Label Shift Adaptation},
author={Ruihan Wu and Siddhartha Datta and Yi Su and Dheeraj Baby and Yu-Xiang Wang and Kilian Q Weinberger},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HNH1ykRjXf}
}
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challenging. While existing methods primarily focus on adjusting or updating the final layer of a pre-trained classifier, we explore the untapped potential of enhancing feature representations using unlabeled data at test-time. Our novel method, Online Label Shift adaptation with Online Feature Updates (OLS-OFU), leverages self-supervised learning to refine the feature extraction process, thereby improving the prediction model. By carefully designing the algorithm, theoretically OLS-OFU maintains the similar online regret convergence to the results in the literature while taking the improved features into account. Empirically, it achieves substantial improvements over existing methods, which is as significant as the gains existing methods have over the baseline (i.e., without distribution shift adaptations).
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
Ruihan Wu Siddhartha Datta Yi Su Dheeraj Baby Yu-Xiang Wang Kilian Q Weinberger