Heteroscedasticity-aware residuals-based contextual stochastic optimization
arXiv:2101.03139
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.
References in corpus (5)
- Task-based End-to-end Model Learning in Stochastic Optimization
- Random design analysis of ridge regression
- Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes
- Nonparametric estimation in a regression model with additive and multiplicative noise
- Learning Heteroscedastic Models by Convex Programming under Group Sparsity