benedekrozemberczki/awesome-decision-tree-papers

Curates papers from 15+ major ML, vision, NLP, and data conferences spanning tree-based methods for explainability, robustness, fairness, and specialized tasks like extreme multi-label ranking. Each paper entry includes direct links to implementations, enabling researchers to reproduce methods across diverse applications from adversarial robustness to counterfactual explanations. The collection emphasizes modern extensions of classical tree algorithms—including neural tangent kernels, Bayesian approaches, and differentiable variants—rather than foundational tree theory.

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