Integrated Optimization of Predictive and Prescriptive Tasks

TL;DR

This paper introduces a novel bilevel optimization framework that integrates predictive and prescriptive tasks, enabling more consistent decision-making by directly training predictive models within prescriptive problems.

Contribution

It proposes a new method that combines predictive modeling and prescriptive decision-making using bilevel optimization, improving decision consistency.

Findings

Demonstrates improved decision quality on synthetic data.

Outperforms classical point-estimate and stochastic methods.

Effectively controls generalization error with penalty approaches.

Abstract

In traditional machine learning techniques, the degree of closeness between true and predicted values generally measures the quality of predictions. However, these learning algorithms do not consider prescription problems where the predicted values will be used as input to decision problems. In this paper, we efficiently leverage feature variables, and we propose a new framework directly integrating predictive tasks under prescriptive tasks in order to prescribe consistent decisions. We train the parameters of predictive algorithm within a prescription problem via bilevel optimization techniques. We present the structure of our method and demonstrate its performance using synthetic data compared to classical methods like point-estimate-based, stochastic optimization and recently developed machine learning based optimization methods. In addition, we control generalization error using different penalty approaches and optimize the integration over validation data set.