The work defines a Selective-Exclusion handoff contract for hierarchical L2D, proves nodewise Bayes rules can be incoherent, and supplies exact dynamic-programming projection and TBP+RPO that drive incoherence to near zero on medical benchmarks.
IEEE Transactions on Information Theory16(1), 41–46 (1970)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Selective prediction abstains unless all Lipschitz-consistent heads in the version space agree on a certified label for each pool point.
citing papers explorer
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Coherent Hierarchical Multi-Label Learning to Defer for Medical Imaging
The work defines a Selective-Exclusion handoff contract for hierarchical L2D, proves nodewise Bayes rules can be incoherent, and supplies exact dynamic-programming projection and TBP+RPO that drive incoherence to near zero on medical benchmarks.
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Selective Prediction from Agreement: A Lipschitz-Consistent Version Space Approach
Selective prediction abstains unless all Lipschitz-consistent heads in the version space agree on a certified label for each pool point.