Lagrangian decomposition yields a scalable surrogate objective and losses for decision-focused learning that outperforms prior DFL methods on large multi-dimensional knapsack and quadratic portfolio instances.
Solver-free decision-focused learning for linear optimization problems
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.LG 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
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.
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.
A tutorial reviewing why traditional prediction models often fail to improve decision quality in stochastic optimization and summarizing key properties and tools of decision-focused learning.
citing papers explorer
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Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
Lagrangian decomposition yields a scalable surrogate objective and losses for decision-focused learning that outperforms prior DFL methods on large multi-dimensional knapsack and quadratic portfolio instances.
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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.
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Decision-Focused Learning: When and Why Traditional Prediction Models Fail
A tutorial reviewing why traditional prediction models often fail to improve decision quality in stochastic optimization and summarizing key properties and tools of decision-focused learning.