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Fast transductive rates in semi-supervised learning can be achieved with fewer labels than previously thought, thanks to the power of data augmentation.
The probability of a model trajectory entering unsafe regions can be exponentially small, but the geometry of the unsafe set critically influences how quickly training stabilizes.
