# Data-Driven Approximations of Chance Constrained Programs in Nonstationary Environments

arXiv: https://arxiv.org/abs/2205.03748 · HTML: https://mlsanity.com/paper/2205.03748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 8 May 2022.

## Builds on (most cited first, 2 of 2)

- [Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations](https://mlsanity.com/paper/1505.05116/) (2015)
- [Randomized Methods for Design of Uncertain Systems: Sample Complexity and Sequential Algorithms](https://mlsanity.com/paper/1304.0678/) (2013)

## Built on by (most cited first, 1 of 1)

- [Risk Analysis and Design Against Adversarial Actions](https://mlsanity.com/paper/2505.01130/) (2025)

## Abstract (arXiv)

We study sample average approximations (SAA) of chance constrained programs. SAA methods typically approximate the actual distribution in the chance constraint using an empirical distribution constructed from random samples assumed to be independent and identically distributed according to the actual distribution. In this paper, we consider a nonstationary variant of this problem, where the random samples are assumed to be independently drawn in a sequential fashion from an unknown and possibly time-varying distribution. This nonstationarity may be driven by changing environmental conditions present in many real-world applications. To account for the potential nonstationarity in the data generation process, we propose a novel robust SAA method exploiting information about the Wasserstein distance between the sequence of data-generating distributions and the actual chance constraint distribution. As a key result, we obtain distribution-free estimates of the sample size required to ensure that the robust SAA method will yield solutions that are feasible for the chance constraint under the actual distribution with high confidence.