Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work unifies the two by establishing a crisp mathematical correspondence between hierarchical decision trees and diffusion processes in appropriate limiting regimes. Our unification reveals a shared optimization principle: \emph{Global Trajectory Score Matching (GTSM)}, for which gradient boosting (in an idealized version) is asymptotically optimal. We underscore the conceptual value of our work through two key practical instantiations: \treeflow, which achieves competitive generation quality on tabular data with higher fidelity and a 2\times co...
| Authors | Sai Niranjan Ramachandran, Suvrit Sra |
|---|---|
| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 21 May 2026 |
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