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XBART: A Novel Tree-Based Machine Learning Framework for Regression, Classification and Treatment Effect Estimation - CityUHK Scholars

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Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review Powered by Pure, Scopus & Elsevier Fingerprint Engine™ All content on this site: Copyright © 2025 CityUHK Scholars, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply We use cookies to help provide and enhance our service and tailor content. By continuing you agree to the use of cookies Log in to CityUHK Scholars CityUHK Scholars data protection policy About web accessibility Report vulnerability

XBART: A Novel Tree-Based Machine Learning Framework for Regression, Classification and Treatment Effect Estimation - CityUHK Scholars Skip to main navigation Skip to search Skip to main content XBART: A Novel Tree-Based Machine Learning Framework for Regression, Classification and Treatment Effect Estimation HE, Jingyu (Principal Investigator / Project Coordinator) Project : Research Project Details Description In the era of big data, huge size and variety of available data becomes new norm in many fields of science. Thus they enable researchers to deploy more complicated machine learning alg

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