Related papers: It's All in the Mix: Wasserstein Machine Learning with Mixed Features

It's All in the Mix: Wasserstein Machine Learning with Mixed Features

URL: http://arxiv.org/abs/2312.12230v1
Date: Tue, 19 Dec 2023 15:15:52 GMT
Title: It's All in the Mix: Wasserstein Machine Learning with Mixed Features
Authors: Reza Belbasi and Aras Selvi and Wolfram Wiesemann
Abstract summary: We present a practically efficient algorithm to solve mixed-feature problems. We demonstrate that our approach can significantly outperform existing methods that are to the presence of discrete features.
Score: 5.739657897440173
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Problem definition: The recent advent of data-driven and end-to-end decision-making across different areas of operations management has led to an ever closer integration of prediction models from machine learning and optimization models from operations research. A key challenge in this context is the presence of estimation errors in the prediction models, which tend to be amplified by the subsequent optimization model -- a phenomenon that is often referred to as the Optimizer's Curse or the Error-Maximization Effect of Optimization. Methodology/results: A contemporary approach to combat such estimation errors is offered by distributionally robust problem formulations that consider all data-generating distributions close to the empirical distribution derived from historical samples, where `closeness' is determined by the Wasserstein distance. While those techniques show significant promise in problems where all input features are continuous, they scale exponentially when binary and/or categorical features are present. This paper demonstrates that such mixed-feature problems can indeed be solved in polynomial time. We present a practically efficient algorithm to solve mixed-feature problems, and we compare our method against alternative techniques both theoretically and empirically on standard benchmark instances. Managerial implications: Data-driven operations management problems often involve prediction models with discrete features. We develop and analyze a methodology that faithfully accounts for the presence of discrete features, and we demonstrate that our approach can significantly outperform existing methods that are agnostic to the presence of discrete features, both theoretically and across standard benchmark instances.