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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
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…
The main features of harshakokel/kigb are: Gradient Boosting Research.
Projects with overlapping indexed features 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.
