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arXiv · 2101.03139

Heteroscedasticity-aware residuals-based contextual stochastic optimization

Rohit Kannan·Güzin Bayraksan·James Luedtke
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Abstract

We explore generalizations of some integrated learning and optimization frameworks for data-driven contextual stochastic optimization that can adapt to heteroscedasticity. We identify conditions on the stochastic program, data generation process, and the prediction setup under which these generalizations possess asymptotic and finite sample guarantees for a class of stochastic programs, including two-stage stochastic mixed-integer programs with continuous recourse. We verify that our assumptions hold for popular parametric and nonparametric regression methods.

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Rohit Kannan, Güzin Bayraksan, James Luedtke. 2021-01-08. Heteroscedasticity-aware residuals-based contextual stochastic optimization. https://arxiv.org/abs/2101.03139

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