Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training

Understand

Self-training is a classical approach in semi-supervised learning which is successfully applied to a variety of machine learning problems.

• Self-training algorithm generates pseudo-labels for the unlabeled examples and progressively refines these pseudo-labels which hopefully coincides with the actual labels.
• This work provides theoretical insights into self-training algorithm with a focus on linear classifiers.
• We first investigate Gaussian mixture models and provide a sharp non-asymptotic finite-sample characterization of the self-training iterations.