Wozniakowski, Alex and Thompson, Jayne and Gu, Mile and Binder, Felix C (2021) A new formulation of gradient boosting. Machine Learning: Science and Technology, 2 (4). 045022. ISSN 2632-2153
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Abstract
In the setting of regression, the standard formulation of gradient boosting generates a sequence of improvements to a constant model. In this paper, we reformulate gradient boosting such that it is able to generate a sequence of improvements to a nonconstant model, which may contain prior knowledge or physical insight about the data generating process. Moreover, we introduce a simple variant of multi-target stacking that extends our approach to the setting of multi-target regression. An experiment on a real-world superconducting quantum device calibration dataset demonstrates that our approach outperforms the state-of-the-art calibration model even though it only receives a paucity of training examples. Further, it significantly outperforms a well-known gradient boosting algorithm, known as LightGBM, as well as an entirely data-driven reimplementation of the calibration model, which suggests the viability of our approach.
Item Type: | Article |
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Subjects: | Middle East Library > Multidisciplinary |
Depositing User: | Unnamed user with email support@middle-eastlibrary.com |
Date Deposited: | 05 Jul 2023 04:31 |
Last Modified: | 07 Sep 2024 10:39 |
URI: | http://editor.openaccessbook.com/id/eprint/1272 |