Awesome Gradient Boosting Papers
A curated collection of gradient boosting research papers with implementations from top machine learning conferences.
What is Awesome Gradient Boosting Papers?
Awesome Gradient Boosting Papers is a comprehensive, community-maintained repository that aggregates academic research on gradient and adaptive boosting methods. It serves as a centralized resource for discovering recent advancements, implementations, and applications across domains like machine learning, computer vision, natural language processing, and data mining. The repository organizes papers from major conferences (e.g., NeurIPS, ICML, CVPR, KDD) from 2009 to 2025, with many entries linking to official code for reproducibility.
This repository is designed for machine learning researchers, practitioners, and graduate students who need to stay updated on state-of-the-art gradient boosting techniques and their implementations. It is particularly useful for those working on specialized applications like fairness, robustness, federated learning, or probabilistic prediction who require access to both theoretical papers and practical code.
Developers choose this project because it provides a curated, structured, and up-to-date collection of gradient boosting research with direct links to implementations, lowering the barrier to applying cutting-edge methods. Its unique value lies in bridging academic research and practical use by organizing papers by year and conference while connecting to related awesome-lists for specialized areas like graph classification and fraud detection.
Overview
A curated list of gradient boosting research papers with implementations.
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