learning to defer

A Causal Target for Learning to Defer Under Hidden Confounding AAAI 2026 Coverage-Constrained Human-AI Cooperation with Multiple Experts AAAI 2026 Towards Robust Human–AI Decision-Making via Learning-to-Defer AAAI 2026 Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees ICML 2025 Things Machine Learning Models Know That They Don’t Know AAAI 2025 Exploiting Human-AI Dependence for Learning to Defer ICML 2024 Learning to Defer to a Population: A Meta-Learning Approach AISTATS 2024 Mitigating Underfitting in Learning to Defer with Consistent Losses AISTATS 2024 Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer NeurIPS 2024 In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer NeurIPS 2023 Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles AISTATS 2023 Learning to Defer with Limited Expert Predictions AAAI 2023 Two-Stage Learning to Defer with Multiple Experts NeurIPS 2023 Who Should Predict? Exact Algorithms For Learning to Defer to Humans AISTATS 2023 Calibrated Learning to Defer with One-vs-All Classifiers ICML 2022 Post-hoc estimators for learning to defer to an expert NeurIPS 2022 Role of Human-AI Interaction in Selective Prediction AAAI 2022 Consistent Estimators for Learning to Defer to an Expert ICML 2020 Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer NeurIPS 2018