Heteroscedasticity-aware residuals-based contextual stochastic optimization
This work provides theoretical guarantees for a class of stochastic optimization problems, which is important for researchers and practitioners working with data-driven decision-making under uncertainty.
This paper generalizes integrated learning and optimization frameworks for data-driven contextual stochastic optimization to adapt to heteroscedasticity. It establishes asymptotic and finite sample guarantees for a class of stochastic programs, including two-stage stochastic mixed-integer programs with continuous recourse, under specific conditions.
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.