Preprint
Aug 2026
No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers
A consistency theory for robust neural classifiers based on the S-divergence family that requires no identifiability assumption is developed and it is proved that empirical S-divergence minimizers converge to the population-optimal equivalence class under mild regularity conditions.
Subhabrata Majumdar, Anand Deo, Partha Pratim Saha et al.
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