Minimax optimal approaches to the label shift problem
Year: 2020
Type: preprint
Abstract: We study minimax rates of convergence in the label shift problem. In addition to the usual setting in which the learner only has access to unlabeled examples from the target domain, we also consider the setting in which a small number of labeled examples from the target domain are available to the learner. Our study reveals a difference in the difficulty of the label shift problem in the two settings. We attribute this difference to the availability of data from the target domain to estimate the class conditional distributions in the latter setting. We also show that a distributional matching approach is minimax rate-optimal in the former setting.
Source: arXiv (Cornell University)
Authors: Subha Maity, Yuekai Sun, Moulinath Banerjee
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3013037761