# Heteroscedasticity-aware residuals-based contextual stochastic optimization

arXiv: https://arxiv.org/abs/2101.03139 · HTML: https://mlsanity.com/paper/2101.03139/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [Adaptive Huber Regression](https://mlsanity.com/paper/1706.06991/) (2017)
- [Residuals-based distributionally robust optimization with covariate information](https://mlsanity.com/paper/2012.01088/) (2020)
- [Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes](https://mlsanity.com/paper/1502.06557/) (2015)
- [A New Perspective on Robust M-Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing](https://mlsanity.com/paper/1711.05381/) (2017)
- [Learning Heteroscedastic Models by Convex Programming under Group Sparsity](https://mlsanity.com/paper/1304.4549/) (2013)
- [Nonparametric estimation in a regression model with additive and multiplicative noise](https://mlsanity.com/paper/1906.07695/) (2019)

## Built on by (most cited first, 5 of 5)

- [A Survey of Contextual Optimization Methods for Decision Making under Uncertainty](https://mlsanity.com/paper/2306.10374/) (2023)
- [Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective](https://mlsanity.com/paper/2304.06833/) (2023)
- [Data-Driven Sample Average Approximation with Covariate Information](https://mlsanity.com/paper/2207.13554/) (2022)
- [Robustifying Conditional Portfolio Decisions via Optimal Transport](https://mlsanity.com/paper/2103.16451/) (2021)
- [Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints](https://mlsanity.com/paper/2505.22881/) (2025)

## Abstract (arXiv)

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.