awesome-decision-tree-papers

Why we included this project

Most of us meet tree models through the scikit-learn or XGBoost docs, which tell you how to call the library but not where the ideas came from. This list fills that gap: it collects decision, classification, and regression tree papers from the major ML, vision, NLP, data mining, and AI conferences, and it favors papers that ship with implementations. Papers are grouped by venue, so you can scan what NeurIPS or KDD published on trees in a given year, or assemble a reading list for a project built on random forests, gradient boosting, or interpretable models. It is a research map rather than something you deploy, but that is exactly what makes it useful for tracing how tree methods evolved and finding the code behind the papers.