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Binary sources allow for strictly smaller optimal representations in the Information Bottleneck than previously thought.
Optimizing representation bottlenecks directly for operational decision error rather than mutual information breaks the classical concavity guarantee, proving that features can be compressed by 83% while retaining 90% of the optimal classification exponent.
