Heteroscedasticity-aware residuals-based contextual stochastic optimization

• URL: http://arxiv.org/abs/2101.03139v1
• Date: Fri, 8 Jan 2021 18:11:21 GMT
• Title: Heteroscedasticity-aware residuals-based contextual stochastic optimization
• Authors: Rohit Kannan and G\"uzin Bayraksan and James Luedtke
• Abstract summary: We explore generalizations of some integrated learning and optimization frameworks for data-driven contextual optimization. We identify conditions on the program, data generation process, and the prediction setup under which these generalizations possess and finite sample guarantees.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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