Open-source alternatives to Gbrl

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

Boosted Descriptors

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

This is the code for the paper titled Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks (arXiv).

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

)](https://pypi.org/project/fipepy/) versions](https://img.shields.io/pypi/pyversions/fipepy.svg)](https://pypi.org/project/fipepy/)

Boosted Generative Models

This repository contains the code and datasets for the paper "Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles".

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:

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)

Implementation of an article

Incorporating richer human inputs including qualitative constraints such as monotonic and synergistic influences has long been adapted inside AI. Inspired by this, we consider the problem of using such influence statements in the successful gradient-boosting framework. We develop a unified…

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

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 repository contains a simple PyTorch implementation of the article Learning Deep ResNet Blocks Sequentially using Boosting Theory.

This is the official clone for the implementation of mGBDT.

Yuan Bian, Grace Y. Yi, and Wenqing He (2025). Boosting methods for interval-censored data with regression and classification. In The 13th International Conference on Learning Representations (ICLR 2025). https://openreview.net/pdf?id=DzbUL4AJPP

NeurIPS 2019

http://www.weibo.com/haoawesome 简介 : 问答服务, 订阅服务, 使用许可 问答与传送档案 通知与声明

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…

This repository contains the codes needed to reproduce the experiments of our submitted CIKM 2025 Paper

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:

AOSOLogitBoost