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Title: A new formulation of gradient boosting
Authors: Wozniakowski, Alex
Thompson, Jane
Gu, Mile
Binder, Felix C.
Keywords: Science::Physics
Issue Date: 2021
Source: Wozniakowski, A., Thompson, J., Gu, M. & Binder, F. C. (2021). A new formulation of gradient boosting. Machine Learning: Science and Technology, 2(4), 045022-.
Project: RG162/19
Journal: Machine Learning: Science and Technology
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.
ISSN: 2632-2153
DOI: 10.1088/2632-2153/ac1ee9
Schools: School of Physical and Mathematical Sciences 
Organisations: Centre for Quantum Technologies, NUS
Research Centres: Complexity Institute 
Rights: © 2021 The Author(s). Published by IOP Publishing Ltd. Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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