LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu published "LightGBM: A Highly Efficient Gradient Boosting Decision Tree" in December 2017. The paper describes LightGBM as a gradient-boosting decision-tree implementation using Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) to improve training efficiency and scalability.