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RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations — arXiv:2610.10214

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10214
License (as archived): CC0-1.0
Archived: 2026-10-08T12:37:27.806Z
SHA-256 of the archived text: 1bbf0528584b76e70049292f07dfe333131c7e97374ca8796dc8e9f4683251fd
Stable URL of this page: https://metraipolis.com/documento/dc_c2dd627100b7

How to cite

RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations — arXiv:2610.10214 — arXiv (en), https://arxiv.org/abs/2610.10214 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c2dd627100b7 (Metraipolis archive dc_c2dd627100b7, sha256 1bbf0528584b76e70049292f07dfe333131c7e97374ca8796dc8e9f4683251fd)

BibTeX

@misc{dcc2dd627100b7,
  title = {RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations — arXiv:2610.10214},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10214},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c2dd627100b7; sha256 1bbf0528584b76e70049292f07dfe333131c7e97374ca8796dc8e9f4683251fd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10214. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Bayesian additive regression trees (BART) can require many splits to approximate boundaries misaligned with the predictor axes. RoBART assigns each tree a rotation shared by all internal nodes, retaining axis-aligned splits in rotated coordinates and constant leaves. We jointly propose a Givens rotation sequence and cutpoints on the resulting grid by Metropolis-Hastings and establish reversibility with respect to the conditional posterior with leaf means integrated out. For additive functions with component-specific rotations and anisotropic Hölder smoothness, we prove posterior contraction in empirical $L_2$ distance and for the noise standard deviation. Under the stated prior, design, and grid conditions, with fixed numbers of predictors, trees, and components and no more components than trees, the rate is a sum of componentwise rates determined by smoothness and the number of rotated coordinates used. We also establish a posterior contraction lower bound showing that there exist functions for which RoBART adapts to the intrinsic dimension but axis-aligned BART does not.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Statistics Theory (math.ST).
Pagina del lavoro: https://arxiv.org/abs/2610.10214 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T12:14:15.456Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).