benedekrozemberczki/awesome-decision-tree-papers
A curated list of research papers on decision trees, classification, and regression algorithms with implementations from top ML conferences.
Not currently ranked — collecting fresh signals.
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This repository aggregates academic research papers on decision tree methods including classification trees, regression trees, and ensemble variants such as gradient boosting machines, XGBoost, LightGBM, and CatBoost. Papers are sourced from major ML, CV, NLP, and AI venues including NeurIPS, ICML, ICLR, CVPR, ACL, AAAI, and KDD. Each paper entry links to implementations where available.
Frequently asked
- What is benedekrozemberczki/awesome-decision-tree-papers?
- A curated list of research papers on decision trees, classification, and regression algorithms with implementations from top ML conferences.
- Is awesome-decision-tree-papers open source?
- Yes — benedekrozemberczki/awesome-decision-tree-papers is open source, released under the CC0-1.0 license.
- What language is awesome-decision-tree-papers written in?
- benedekrozemberczki/awesome-decision-tree-papers is primarily written in Python.
- How popular is awesome-decision-tree-papers?
- benedekrozemberczki/awesome-decision-tree-papers has 2.5k stars on GitHub.
- Where can I find awesome-decision-tree-papers?
- benedekrozemberczki/awesome-decision-tree-papers is on GitHub at https://github.com/benedekrozemberczki/awesome-decision-tree-papers.