# Sharded Bayesian Additive Regression Trees

arXiv: https://arxiv.org/abs/2306.00361 · HTML: https://mlsanity.com/paper/2306.00361/

## 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 (most cited first, 15 of 15)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Coresets for Scalable Bayesian Logistic Regression](https://mlsanity.com/paper/1605.06423/) (2016)
- [A general framework for Vecchia approximations of Gaussian processes](https://mlsanity.com/paper/1708.06302/) (2017)
- [Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software](https://mlsanity.com/paper/2302.11474/) (2023)
- [bartMachine: Machine Learning with Bayesian Additive Regression Trees](https://mlsanity.com/paper/1312.2171/) (2013)
- [Distributed linear regression by averaging](https://mlsanity.com/paper/1810.00412/) (2018)
- [On Theory for BART](https://mlsanity.com/paper/1810.00787/) (2018)
- [Bayesian experimental design using regularized determinantal point processes](https://mlsanity.com/paper/1906.04133/) (2019)
- [Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems](https://mlsanity.com/paper/1908.05287/) (2019)
- [Parallelizing MCMC via Weierstrass Sampler](https://mlsanity.com/paper/1312.4605/) (2013)
- [Non-smooth Bayesian Optimization in Tuning Problems](https://mlsanity.com/paper/2109.07563/) (2021)
- [A Distance-preserving Matrix Sketch](https://mlsanity.com/paper/2009.03979/) (2020)
- [Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes](https://mlsanity.com/paper/2211.14692/) (2022)
- [The Mondrian Process for Machine Learning](https://mlsanity.com/paper/1507.05181/) (2015)
- [Bayesian Additive Regression Trees With Parametric Models of Heteroskedasticity](https://mlsanity.com/paper/1402.5397/) (2014)

## Built on by (most cited first, 6 of 6)

- [A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes](https://mlsanity.com/paper/2309.10068/) (2023)
- [Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data](https://mlsanity.com/paper/2411.05869/) (2024)
- [Efficient Decision Trees for Tensor Regressions](https://mlsanity.com/paper/2408.01926/) (2024)
- [Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selection](https://mlsanity.com/paper/2410.02623/) (2024)
- [GGMPs: Generalized Gaussian Mixture Processes](https://mlsanity.com/paper/2603.10442/) (2026)
- [Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compression](https://mlsanity.com/paper/2603.26048/) (2026)

## 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.