---
title: "Optimization Under Epistemic Uncertainty Using Prediction"
authors:
  - "Noah Schutte"
doi: "10.24963/ijcai.2024/967"
url: "https://ijcai.org/proceedings/2024/967"
pdf: "https://ijcai.org/proceedings/2024/0967.pdf"
venue: "Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence"
year: 2024
pages: "8504-8505"
track: "Doctoral Consortium"
keywords:
  - "DC: Machine Learning"
  - "DC: Uncertainty in AI"
  - "DC: Constraint Satisfaction and Optimization"
---

# Optimization Under Epistemic Uncertainty Using Prediction

Noah Schutte

*Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence*, pages 8504-8505. DOI: [10.24963/ijcai.2024/967](https://doi.org/10.24963/ijcai.2024/967)

## Abstract

Due to the complexity of randomness, optimization problems are often modeled to be deterministic to be solvable. Specifically epistemic uncertainty, i.e., uncertainty that is caused due to a lack of knowledge, is not easy to model, let alone easy to subsequently solve. Despite this, taking uncertainty into account is often required for optimization models to produce robust decisions that perform well in practice. We analyze effective existing frameworks, aiming to improve robustness without increasing complexity. Specifically we focus on robustness in decision-focused learning, which is a framework aimed at making context-based predictions for an optimization problem's uncertain parameters that minimize decision error.