A Fiber Criterion for Representation Identifiability in Supervised Learning

ArXi:2606.01092v1 Announce Type: cross Supervised learning evaluates predictors through their input-output behavior. When a predictor is implemented as a composition $f=c\circ h$, supervised evidence constrains the composite map $f$ but need not determine the representation-head factorization $(h,c