This repository is a curated list of research papers on gradient boosting and adaptive boosting algorithms, spanning machine learning, computer vision, natural language processing, and data mining conferences. It serves researchers and practitioners interested in the theoretical and applied advancements in boosting techniques, providing direct links to papers and, where available, their implementations.

How It Works

The collection is organized chronologically by year, starting from 1994, and categorizes papers by the conferences where they were published. This structure allows users to trace the evolution of boosting algorithms and identify key contributions from major research venues. Each entry includes a title, authors, and a link to the paper, with some entries also linking to associated code.

Quick Start & Requirements

No installation or specific software is required to use this repository. It is a static list of links.

Highlighted Details

• Comprehensive coverage of gradient boosting research from 1994 to 2023.
• Includes papers from top-tier conferences across multiple domains (NeurIPS, ICML, CVPR, ACL, KDD, etc.).
• Links to implementations (code) are provided for a subset of the papers.
• Covers a wide range of boosting variations and applications, including fairness, robustness, and specific domains like computer vision and NLP.

Maintenance & Community

The repository is maintained by benedekrozemberczki. No specific community channels or roadmap are indicated.

Licensing & Compatibility

The repository is licensed under CC0 Universal, meaning it is in the public domain and can be used freely for any purpose.

Limitations & Caveats

The repository is a curated list and does not provide any code or implementations itself. The availability of associated code depends on the original authors and is not guaranteed for all listed papers.