Presents the first online Learning-to-Defer algorithm achieving regret O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
Towards unbiased and accurate deferral to multiple experts
2 Pith papers cite this work. Polarity classification is still indexing.
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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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Online Learning-to-Defer with Varying Experts
Presents the first online Learning-to-Defer algorithm achieving regret O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
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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.