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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2026 2verdicts
UNVERDICTED 2representative citing papers
MILD reformulates two-stage learning to defer as cost-sensitive learning over the input-expert domain and derives new margin-based losses with guarantees, yielding better performance than baselines on image classification and LLM routing tasks.
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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Optimized Deferral for Imbalanced Settings
MILD reformulates two-stage learning to defer as cost-sensitive learning over the input-expert domain and derives new margin-based losses with guarantees, yielding better performance than baselines on image classification and LLM routing tasks.