From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
The discussion of 'From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift' explores a new framework, S-PUNA. This method addresses instability in traditional positive-unlabeled (PU) learning when data distributions change (covariate shift). The hosts conclude that S-PUNA offers robust performance across both near and far shifts, enabling reliable AI without needing fully supervised training.