Please use this identifier to cite or link to this item: https://doi.org/10.1088/2632-2153/ac1ee9
Title: A new formulation of gradient boosting
Authors: Wozniakowski, Alex
Thompson, Jayne 
Gu, Mile 
Binder, Felix C.
Keywords: Boosting
Ensemble learning
Multi-target regression
Prior knowledge
Quantum computing
Stacking
Issue Date: 18-Aug-2021
Publisher: IOP Publishing Ltd
Citation: Wozniakowski, Alex, Thompson, Jayne, Gu, Mile, Binder, Felix C. (2021-08-18). A new formulation of gradient boosting. Machine Learning: Science and Technology 2 (4) : 045022. ScholarBank@NUS Repository. https://doi.org/10.1088/2632-2153/ac1ee9
Rights: Attribution 4.0 International
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. © 2021 The Author(s). Published by IOP Publishing Ltd
Source Title: Machine Learning: Science and Technology
URI: https://scholarbank.nus.edu.sg/handle/10635/232709
ISSN: 2632-2153
DOI: 10.1088/2632-2153/ac1ee9
Rights: Attribution 4.0 International
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