How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
NeurIPS 2019
The main features of max-andr/provably-robust-boosting are: Gradient Boosting Research.
Open-source alternatives to max-andr/provably-robust-boosting include: ajoo/nrgboost — This repository contains the official code of the paper NRGBoost: Energy-Based Generative Boosted Trees (ICLR 2025). amirsaffari/online-multiclass-lpboost — Online Multi-Class LPBoost. andymiller/vboost — code for Variational Boosting: Iteratively Refining Posterior Approximations. anitan0925/resfgb — Remark: The code is updated from the ICML version. The ICML version corresponds to a commit on May 25, 2018. arogozhnikov/infiniteboost — Code for a paper InfiniteBoost: building infinite ensembles with gradient descent (arXiv:1706.01109). A. Rogozhnikov,… aciditeam/acidano.
This repository contains the official code of the paper NRGBoost: Energy-Based Generative Boosted Trees (ICLR 2025).
Online Multi-Class LPBoost
code for Variational Boosting: Iteratively Refining Posterior Approximations
