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An Identifiability Theory of Masked Prediction: Mode Blindness and Mask Schedules
Masked prediction learns by inferring missing variables from visible context. When does optimizing this conditional task recover the true joint data distribution? We study this question using an $\varepsilon$-identifiability modulus, which measures the worst-case joint-distribution error permitted b
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