// the find
benedekrozemberczki/awesome-decision-tree-papers
A collection of research papers on decision, classification and regression trees with implementations.
A bibliography of decision tree and tree ensemble papers from major ML conferences (NeurIPS, ICML, KDD, etc.) from 2015 through 2022, with links to papers and implementations where available. Useful for researchers or practitioners who want to know what the academic community has been working on in this space. Not a tutorial, not a library — a reading list.
Coverage is genuinely broad across venues: it includes not just ML conferences but CV (CVPR, ICCV), NLP (ACL, EMNLP), and data mining (KDD, CIKM, ICDM), which catches work on tree methods that ML-only lists miss. Implementation links are included where they exist, so you can go straight from paper to code. The historical depth (2015–2022) makes it useful for tracing how ideas like optimal decision trees and adversarial robustness evolved. The companion repos (gradient boosting, Monte Carlo tree search, fraud detection) are consistently structured, so if you like this one, the others are directly navigable.
Coverage stops at 2022 — nothing from 2023 or 2024, which is a meaningful gap given how much work on tree methods in the LLM era (TabPFN successors, tree-based feature extractors for tabular foundation models) has appeared since. Many entries have no code link at all, and there's no indication of whether linked repos are maintained or abandoned. The list has no annotations or commentary — every paper gets equal weight whether it's XGBoost or a niche SDM paper nobody cited. If you're trying to figure out which 10 papers to actually read, this gives you no signal.