Computer Science > Machine Learning
[Submitted on 14 Apr 2022 (v1), last revised 7 Jun 2023 (this version, v2)]
Title:Gradient boosting for convex cone predict and optimize problems
View PDFAbstract:Prediction models are typically optimized independently from decision optimization. A smart predict then optimize (SPO) framework optimizes prediction models to minimize downstream decision regret. In this paper we present dboost, the first general purpose implementation of smart gradient boosting for `predict, then optimize' problems. The framework supports convex quadratic cone programming and gradient boosting is performed by implicit differentiation of a custom fixed-point mapping. Experiments comparing with state-of-the-art SPO methods show that dboost can further reduce out-of-sample decision regret.
Submission history
From: Andrew Butler [view email][v1] Thu, 14 Apr 2022 11:47:19 UTC (292 KB)
[v2] Wed, 7 Jun 2023 16:12:41 UTC (283 KB)
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