Related Research
Additional studies referenced include work on optimizing data mixtures for large foundation models, presented at NeurIPS 2025, which addresses the challenge of selecting domain proportions for training data without relying on trial and error. Another related paper explores the problem of loss prediction in classification tasks, focusing on how well predictors can estimate their own incurred loss, a key aspect of uncertainty estimation.
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