MTSSL: A New Approach to Threshold Selection in Semi-Supervised Learning
A new preprint introduces Meta-Thresholding Semi-Supervised Learning (MTSSL), a framework that treats the threshold parameter ($\tau$) in semi-supervised learning as an optimizable variable rather than a fixed hyperparameter. The authors provide a unified theoretical explanation for the role of $\tau$, showing that different values can yield similar performance, and demonstrate through experiments that MTSSL achieves strong results. Their findings suggest that precise tuning of $\tau$ may be unnecessary, potentially simplifying future SSL algorithm design.
Why it matters: This work could make semi-supervised learning methods more robust and easier to use by reducing the need for manual threshold tuning.
Full story at: arXiv Statistical ML ↗