Develops the first adversarial robustness framework for one-stage learning-to-defer, including cost-sensitive surrogate losses and theoretical consistency guarantees for classification and regression.
Adversarial robustness in two-stage learning-to-defer: Algorithms and guarantees, 2025
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A learning-to-defer framework allocates extractive QA queries to LLM experts with theoretical optimality guarantees, shown to improve reliability and cut overhead on SQuAD and TriviaQA.
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Adversarial Robustness in One-Stage Learning-to-Defer
Develops the first adversarial robustness framework for one-stage learning-to-defer, including cost-sensitive surrogate losses and theoretical consistency guarantees for classification and regression.
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Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees
A learning-to-defer framework allocates extractive QA queries to LLM experts with theoretical optimality guarantees, shown to improve reliability and cut overhead on SQuAD and TriviaQA.