LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu, 2017Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc.) - The original academic paper introducing LightGBM and detailing the Gradient-based One-Side Sampling (GOSS) algorithm.
LightGBM Parameters, LightGBM Contributors, 2024 - Official LightGBM documentation providing current practical details and configuration parameters for GOSS, including top_rate and other_rate.
Stochastic Gradient Boosting, Jerome H. Friedman, 2002Computational Statistics & Data Analysis, Vol. 38 (Elsevier)DOI: 10.1016/S0167-9473(01)00065-2 - This foundational paper introduces the concept of stochastic gradient boosting, using subsampling to improve computational efficiency and generalization, providing valuable context for understanding GOSS's distinctions.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009 (Springer) - A comprehensive textbook that provides a theoretical foundation for gradient boosting machines and other ensemble learning methods, relevant for understanding the principles behind GOSS.