This repository aggregates academic papers on gradient boosting and adaptive boosting algorithms from top machine learning venues including NeurIPS, ICML, and ICLR. It includes implementations and covers major frameworks like XGBoost, LightGBM, and CatBoost alongside foundational techniques such as AdaBoost, decision trees, and random forests.