Hugging Face Trending Papers
RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Machine Learning
Oct 8
RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations
arXiv:2610.10214v1 Announce Type: cross Abstract: Bayesian additive regression trees (BART) can require many splits to approximate boundaries misaligned with the predictor axes. RoBART assigns each t...
arXiv Machine Learning
Sep 24
Dirichlet Process Mixtures of Trees with Gaussian Process Splits: A Bayesian Nonparametric Framework with Posterior Contraction Rate
arXiv:2609. 27930v1 Announce Type: cross Abstract: We propose a Bayesian nonparametric mixture of regression trees with a Dirichlet process prior over tree-parameter pairs, enabling data-driven selection of ensemble size and unifying CART, BART, random forests, and boosting.
arXiv Statistics ML
Sep 2
Seeing the Forest for the Trees: The Gaussian Process Limit of BART
arXiv:2607.28844v2 Announce Type: replace-cross Abstract: Bayesian Additive Regression Trees (BART) have shown state-of-the-art performance in both prediction and causal inference problems. Previous...
arXiv Statistics ML
Sep 17
Functional BART with Shape Priors: A Bayesian Tree Approach to Constrained Functional Regression
arXiv:2502.16888v3 Announce Type: replace-cross Abstract: Motivated by the remarkable success of Bayesian additive regression trees (BART) in regression modelling, we propose a novel nonparametric Ba...
arXiv Machine Learning
Aug 18
On Stopping Rules and Spatial Adaptation for CART
arXiv:2608. 15649v1 Announce Type: cross Abstract: The popular CART algorithm for regression trees combines a greedy splitting rule with a stopping rule, but while the splitting rule has been well studied, the statistical role of stopping rules is less well understood.
arXiv Statistics ML
Oct 5
A Residual Tree Gaussian Process Modeling Framework for High-Dimensional Data
The paper introduces ResTGP, a Bayesian residual tree Gaussian Process framework designed for large, high‑dimensional spatial datasets with heterogeneous structures. It decomposes a Gaussian process across a dyadic tree, iteratively computing predictive and residual processes at each node to capture multi‑scale covariance efficiently. The method employs recursive message passing for linear‑time Bayesian inference and demonstrates posterior consistency, with numerical experiments and a storm surge application highlighting its advantages.