A curated list of gradient boosting research papers with implementations.

Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R

Represent trained machine learning models as Pyomo optimization formulations

Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark

A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)

Library for Multi-objective optimization in Gradient Boosted Trees

Stochastic tree ensembles (BART / XBART) for supervised learning and causal inference

Multiobjective black-box optimization using gradient-boosted trees

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

Machine Learning Tool Box

Influence Estimation for Gradient-Boosted Decision Trees

OncoNetExplainer: Explainable Prediction of Cancer Types Based on Gene Expression Data

Gradient boosting for OCaml using the R xgboost package under the carpet

ML models trained on the SARCOS dataset

Analysis of information about startup companies done using machine learning and data analytics methods to predict the success of the startup companies.

PhishyAI trains ML models for Phishy, a Gmail extension which leverages ML to detect phishing attempts in all incoming emails

Sequential skip prediction using deep learning and ensembles

An implementation of the algorithms from the camera-ready version of the paper "Coresets for Decision Trees of Signals" (NeurIPS'2021) by Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan Newman, and Dan Feldman.

Machine learning multiclassification task in particle physics experiment (Belle II) with deep neural networks (DNN) and gradient boosted decision trees (XGBoost).

Binary text difficulty classification with tf-idf, word2vec, and other linguistic features with multinomial naive bayes, logistic regression, and gradient boosted decision trees.