Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling

Understand

In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited.

• There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to improve estimation or prediction.
• However, current SSL literature focuses primarily on settings where labeled data is selected randomly from the population of interest.
• Non-random sampling, while posing additional analytical challenges, is highly applicable to many real world problems.