Awesome GitHub RepositoriesGradient Boosting Research

XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m

CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu

Natural Gradient Boosting for Probabilistic Prediction

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Code for a paper InfiniteBoost: building infinite ensembles with gradient descent (arXiv:1706.01109). A. Rogozhnikov, T. Likhomanenko

This is the implementation for the paper Gradient Boosting with Piece-Wise Linear Regression Trees. We extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees. PL Trees can accelerate convergence of GBDT. Moreover, our new…

Code for DeepBoost algorithm described in:

GBRL is a Python-based Gradient Boosting Trees (GBT) library, similar to popular packages such as XGBoost, CatBoost, but specifically designed and optimized for reinforcement learning (RL). GBRL is implemented in C++/CUDA aimed to seamlessly integrate within popular RL libraries.

QuickRank: A C++ suite of Learning-to-Rank algorithms

This is the official clone for the implementation of mGBDT.

A generic recommender and predictor server for both offline machine learning and recommendation modeling and fast online production serving. MIT licence, oriented to production use (online field experiments in research and typical industrial use)

This project implements the AdaGAN algorithm, presented in this paper.

Online Multi-Class LPBoost

NeurIPS 2019

This is a boosting based transfer learning algorithm for regression tasks (TwoStageTrAdaBoostR2) that is proposed by Pardoe et al. in paper "Boosting for Regression Transfer (ICML 2010)". The program TwoStageTrAdaBoostR2 contains two main classes that are written in scikit-learn style and the…

This project is a cleaned up version of our PAMI submission "Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly" in tensorflow. It extends our original ICCV version with an adversarial auxiliary loss during training, which improves results. you are planning to use this…

An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).

BoostSRL (Boosting for Statistical Relational Models) is a gradient-boosting based approach to learning different types of SRL models.

Remark: The code is updated from the ICML version. The ICML version corresponds to a commit on May 25, 2018.

The tutorial 'CalibratedAdaMEC_ExtendedVersion.ipynb' introduces the concepts of asymmetric (cost-sensitive and/or imbalanced class) learning, decision theory and boosting. It briefly describes the results of the paper: