XGBoost: A Scalable Tree Boosting System, Tianqi Chen and Carlos Guestrin, 2016KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM)DOI: 10.1145/2939672.2939785 - The original academic paper that introduces the XGBoost algorithm, comprehensively detailing its regularized objective function, the use of second-order Taylor expansion for optimization, and the specific regularization terms discussed in the section.
Introduction to Boosted Trees, XGBoost Contributors, 2022 (XGBoost Project) - The official XGBoost documentation that explains the theoretical underpinnings of tree boosting, including the derivation of the objective function, the role of Taylor expansion, and the impact of regularization parameters.
Greedy Function Approximation: A Gradient Boosting Machine, Jerome H. Friedman, 2001The Annals of Statistics, Vol. 29DOI: 10.1214/aos/1013203451 - A foundational paper on Gradient Boosting Machines (GBM), providing the statistical framework for additive modeling and the use of gradient descent, which forms the basis that XGBoost builds upon and enhances.