Related papers: Learning Joint Models of Prediction and Optimization

Learning Joint Models of Prediction and Optimization

URL: http://arxiv.org/abs/2409.04898v1
Date: Sat, 7 Sep 2024 19:52:14 GMT
Title: Learning Joint Models of Prediction and Optimization
Authors: James Kotary, Vincenzo Di Vito, Jacob Cristopher, Pascal Van Hentenryck, Ferdinando Fioretto,
Abstract summary: Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
Score: 56.04498536842065
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from exogenous features before solving. This setting is common to many real-world decision processes, and recently it has been shown that decision quality can be substantially improved by solving and differentiating the optimization problem within an end-to-end training loop. However, this approach requires significant computational effort in addition to handcrafted, problem-specific rules for backpropagation through the optimization step, challenging its applicability to a broad class of optimization problems. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models. The approach is generic, and based on an adaptation of the Learning-to-Optimize paradigm, from which a rich variety of existing techniques can be employed. Experimental evaluations show the ability of several Learning-to-Optimize methods to provide efficient and accurate solutions to an array of challenging Predict-Then-Optimize problems.

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