mlsanityData to 14 Oct 2026

Sharded Bayesian Additive Regression Trees

arXiv 2306.00361 ↗First posted 1 Jun 2023stat.ML cs.LG math.STcited by 6

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Abstract (arXiv)

In this paper we develop the randomized Sharded Bayesian Additive Regression Trees (SBT) model. We introduce a randomization auxiliary variable and a sharding tree to decide partitioning of data, and fit each partition component to a sub-model using Bayesian Additive Regression Tree (BART). By observing that the optimal design of a sharding tree can determine optimal sharding for sub-models on a product space, we introduce an intersection tree structure to completely specify both the sharding and modeling using only tree structures. In addition to experiments, we also derive the theoretical optimal weights for minimizing posterior contractions and prove the worst-case complexity of SBT.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 15 of its 52 references that are in the map. First posted on arXiv 1 Jun 2023.

Builds on

15 of 52 references.

References · 15

  1. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  2. 2016Coresets for Scalable Bayesian Logistic Regressioncited by 79
  3. 2017A general framework for Vecchia approximations of Gaussian processescited by 67
  4. 2023Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Softwarecited by 38
  5. 2013bartMachine: Machine Learning with Bayesian Additive Regression Treescited by 24
  6. 2018Distributed linear regression by averagingcited by 21
  7. 2018On Theory for BARTcited by 17
  8. 2019Bayesian experimental design using regularized determinantal point processescited by 17
  9. 2019Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problemscited by 15
  10. 2013Parallelizing MCMC via Weierstrass Samplercited by 11
  11. 2021Non-smooth Bayesian Optimization in Tuning Problemscited by 8
  12. 2020A Distance-preserving Matrix Sketchcited by 4
3 more
  1. 2022Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processescited by 4
  2. 2015The Mondrian Process for Machine Learningcited by 3
  3. 2014Bayesian Additive Regression Trees With Parametric Models of Heteroskedasticitycited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 2.

6 papers.

  1. 2023A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processescited by 8
  2. 2024Compactly-supported nonstationary kernels for computing exact Gaussian processes on big datacited by 2
  3. 2024Efficient Decision Trees for Tensor Regressionscited by 1
  4. 2024Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selectioncited by 1
  5. 2026GGMPs: Generalized Gaussian Mixture Processescited by 1
  6. 2026Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compressioncited by 0