This repository collects gradient boosting research papers with implementations.
Open source
24 alternativesAwesome Decision Tree Papers
This repository collects decision tree research papers with implementations.
About Awesome Decision Tree Papers
Awesome Decision Tree Papers is a GitHub repository that collects research papers on decision, classification and regression trees with implementations. Files include a README, license, and an awesome.py script.
It provides a curated list organized by conferences including NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, NAACL, EMNLP, KDD, CIKM, ICDM, SDM, PAKDD, PKDD/ECML, SIGIR, WWW, WSDM, AAAI, AISTATS, ICANN, IJCAI and UAI. Entries include paper titles, authors, venue and year, with links to papers and to code where available. The repository also points to similar collections on graph classification, gradient boosting, fraud detection, Monte Carlo tree search and community detection.
It suits researchers, students and developers who work with tree-based models and want to find relevant literature and implementations in one place.
Key features
- Curated list of decision, classification and regression tree papers
- Links to papers with code implementations where available
- Entries organized by venue and year with author details
- Covers NeurIPS, ICML, ICLR, AAAI and other conferences
- Links to related collections on gradient boosting and other topics
Best for: Researchers, students, and developers seeking decision tree papers with implementations.
Good for
- Open source repository
- Focused specifically on tree-based methods
- Includes paper and code links
- Covers many major research venues
Awesome Decision Tree Papers at a glance
- Category
- AI & Machine Learning › AI Search & Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Awesome Decision Tree Papers FAQ
What is Awesome Decision Tree Papers?
It is a GitHub repository collecting research papers on decision, classification and regression trees with implementations.
Does it include code implementations?
Yes, many entries provide Paper links and Code links where available.
Which conferences are covered?
It lists papers from venues including NeurIPS, ICML, ICLR, AAAI, KDD, WWW, CVPR and others.
Is the repository open source?
Yes, it is listed as open source and includes a LICENSE file.
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