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.
Two-stage learning-to-defer for multi-task learning, 2024
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
citing papers explorer
-
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.
- Adversarial Robustness in One-Stage Learning-to-Defer
- Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees