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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AbICL adapts antibody affinity ranking at test time by conditioning on labeled pairwise comparisons through a transformer context head trained with episodic meta-learning, achieving improved AUROC on the AbRank benchmark.
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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.
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AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking
AbICL adapts antibody affinity ranking at test time by conditioning on labeled pairwise comparisons through a transformer context head trained with episodic meta-learning, achieving improved AUROC on the AbRank benchmark.