A decoupled surrogate separates class posterior estimation from per-expert utility estimation, yielding a J-independent H-consistency bound and avoiding the amplification, starvation, and coupling issues of prior augmented-action surrogates.
Generalized cross entropy loss for training deep neural networks with noisy labels
2 Pith papers cite this work. Polarity classification is still indexing.
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CMRM adds a conformal quantile regularization on prediction margins to any loss, improving noisy-label classification accuracy up to 3.39% across methods and benchmarks while preserving performance at zero noise.
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Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer
A decoupled surrogate separates class posterior estimation from per-expert utility estimation, yielding a J-independent H-consistency bound and avoiding the amplification, starvation, and coupling issues of prior augmented-action surrogates.
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Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
CMRM adds a conformal quantile regularization on prediction margins to any loss, improving noisy-label classification accuracy up to 3.39% across methods and benchmarks while preserving performance at zero noise.