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.
Multi-class h -consistency bounds
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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.