Enforcing per-sample loss bounds during training yields more concentrated loss distributions than average-loss training, but the effect varies by task and needs careful tuning of a new threshold.
Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications
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Feasible Learning
Enforcing per-sample loss bounds during training yields more concentrated loss distributions than average-loss training, but the effect varies by task and needs careful tuning of a new threshold.