Diff2SP is a diffusion-based generative model that embeds stochastic optimization objectives into scenario generation and supplies regret bounds plus sample-complexity guarantees relative to GANs.
arXiv preprint arXiv:2210.01802 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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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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Diff2SP: Diffusion Models for Correlated Scenario Generation in Stochastic Programming
Diff2SP is a diffusion-based generative model that embeds stochastic optimization objectives into scenario generation and supplies regret bounds plus sample-complexity guarantees relative to GANs.
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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.