Reframing decision-focused learning as cost-sensitive multi-output regression with cost-insensitive normalization, decision-aware asymmetric penalization, and instance-based costs enables scalable training with comparable task quality but far fewer optimization solves.
arXiv preprint arXiv:2505.22224 , year=
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PEAR computes regret gradients via tangent-space projection of prediction error, delivering top decision quality and efficiency on LP and QP tasks without solver differentiation.
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Scalable Decision-Focused Learning through Cost-Sensitive Regression
Reframing decision-focused learning as cost-sensitive multi-output regression with cost-insensitive normalization, decision-aware asymmetric penalization, and instance-based costs enables scalable training with comparable task quality but far fewer optimization solves.
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Decision-Focused Learning via Tangent-Space Projection of Prediction Error
PEAR computes regret gradients via tangent-space projection of prediction error, delivering top decision quality and efficiency on LP and QP tasks without solver differentiation.