A semiparametric ranking framework models log-scores as utility parameters plus neural-network covariate effects, with proven identifiability and minimax-optimal non-asymptotic error bounds under random design.
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Deep Ranking with Heterogeneous Effects
A semiparametric ranking framework models log-scores as utility parameters plus neural-network covariate effects, with proven identifiability and minimax-optimal non-asymptotic error bounds under random design.