BARMPy offers a Python package for Bayesian Additive Regression Models.
problem Making complex Bayesian models accessible to machine learning practitioners.
method Object-oriented design compatible with SciKit-Learn, documentation and tutorial provided.
result Ease of use and compatibility with existing machine learning tools.
SBT model uses randomized sharding and sub-models to improve Bayesian Additive Regression Trees.
problem Improving efficiency and accuracy of Bayesian Additive Regression Trees.
method Randomized sharding, sub-models, intersection tree structure, optimal design.
result Theoretical optimal weights and worst-case complexity of SBT model.
Bayesian model estimates treatment effects near cutoffs in regression discontinuity designs.
problem Estimating conditional average treatment effects in regression discontinuity designs.
method Develops a Bayesian additive regression tree (BART) model with linear leaf-level regressions.
result Adapts to different slopes on the running variable near the cutoff, providing interpretable inference.
GPU-accelerated BART speeds up Bayesian regression.
problem Long running time of BART makes it impractical for large datasets.
method GPU-enabled implementation of BART.
result BART is now 200x faster on GPUs.
Bayesian Additive Distribution Regression (DistBART) predicts distributions from grouped data.
problem Predicting distributions from grouped data with varying characteristics.
method Bayesian nonparametric approach using BART for modeling the regression function.
result Empirical and theoretical evidence supports DistBART's effectiveness in learning from low-dimensional marginals.
Bayesian Additive Regression Networks use neural networks for regression tasks.
problem Regression tasks with small neural networks and ensemble learning.
method Bayesian Additive Regression Tree principles applied to small neural networks, Gibbs sampling for ensemble learning.
result BARN provides more consistent and often more accurate results than shallow neural networks, BART, and ordinary least squares.
New algorithm broadens BART models applicability.
problem Limited applicability of Bayesian additive regression trees (BART) models due to conditional conjugacy.
method Introduces a reversible jump Markov chain Monte Carlo algorithm for generalized BART models.
result Extends BART models to arbitrary generalized BART models without conditional conjugacy.
Bayesian approach for estimating heterogeneous treatment effects in RDD designs.
problem Heterogeneity in treatment effects in RDD designs can lead to misleading conclusions.
method Direct Bayesian Additive Regression Trees (BART) for modeling heterogeneous treatment effects.
result Flexibly captures complicated structures of heterogeneous treatment effects as a function of covariates.
This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. T…
GP-BART improves BART's predictive performance by incorporating Gaussian process priors.
problem Lack of smoothness and explicit covariance structure in BART.
method GP-BART extends BART with Gaussian process priors for tree predictions.
result GP-BART outperforms traditional models in various applications.
Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much attention despite the increasing popularity of the linear quantile regression model. This work proposes a…
This study converts BART to Gaussian process regression, revealing its limitations and potential improvements.
problem Understanding the Gaussian process limit of BART and its implications.
method Deriving and computing BART's prior covariance function, implementing the infinite trees limit as GP regression, and tuning hyperparameters.
result The Gaussian process limit of BART is inferior to standard BART but can be made competitive with proper hyperparameter tuning.
BARK optimizes black-box functions using Bayesian Additive Regression Trees.
problem Bayesian optimization of complex, black-box functions with uncertainty quantification.
method BART Kernel using tree agreement for posterior over piecewise-constant functions, explored using MCMC.
result BARK obtains samples of Gaussian processes for function distributions, enabling acquisition functions for optimization.
BART is extended to handle various response variables.
problem Modeling nonlinear regression functions for diverse response types.
method Generalized Bayesian Additive Regression Trees (GBART) for exponential family distributions.
result The posterior concentrates at a minimax rate for certain response distributions.
Normalizing flow regression approximates posterior distributions without additional sampling.
problem Bayesian inference with computationally expensive likelihood evaluations.
method Normalizing flow regression (NFR) for offline inference.
result NFR yields a tractable posterior approximation through regression on existing log-density evaluations.
In this paper we utilize a survival analysis methodology incorporating Bayesian additive regression trees to account for nonlinear and additive covariate effects. We compare the performance of Bayesian additive regression trees, Cox proportional hazards and random survival forests models for censored survival data, usi…
Bayesian nonparametric machine learning improves instrumental variable inference.
problem Estimating causal effects with nonlinear relationships.
method Bayesian Additive Regression Trees (BART) for estimating functions and Dirichlet Process mixtures for error terms.
result Dramatic improvements in inference with nonlinear data, no manual tuning required.
ASBART accelerates Soft BART for faster Bayesian regression.
problem Slow computation in Soft BART.
method Proposed ASBART, a variant of Soft BART.
result ASBART is about 10 times faster than Soft BART with similar accuracy.
We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is …
BFTS uses Bayesian Additive Regression Trees for improved personalized mobile health interventions.
problem Adapting to complex, non-linear user behaviors in personalized mobile health interventions.
method Bayesian Forest Thompson Sampling (BFTS) integrates Bayesian Additive Regression Trees (BART) into the exploration loop of contextual bandits.
result BFTS achieves state-of-the-art regret on tabular benchmarks and improves engagement rates by over 30% in a behavioral intervention study.
Oblique BART improves tree-based predictions.
problem Axis-aligned decision rules in BART can be suboptimal.
method Developed an oblique version of BART using data-adaptive hyperplane partitions.
result Oblique BART outperformed axis-aligned BART and other tree methods on benchmarks.
Bayesian Additive Regression Trees (BART) is a fully Bayesian approach to modeling with ensembles of trees. BART can uncover complex regression functions with high dimensional regressors in a fairly automatic way and provide Bayesian quantification of the uncertainty through the posterior. However, BART assumes IID nor…
SoftBart improves BART for high-noise modeling in science.
problem High noise in scientific data.
method Soft BART algorithm for Bayesian additive regression trees.
result Improves predictive performance and facilitates larger model integration.
Using ensemble methods for regression has been a large success in obtaining high-accuracy prediction. Examples are Bagging, Random forest, Boosting, BART (Bayesian additive regression tree), and their variants. In this paper, we propose a new perspective named variable grouping to enhance the predictive performance. Th…
The paper introduces a new model to correct bias in treatment effect estimates due to sample selection.
problem Bias in treatment effect estimates due to sample selection.
method Type 2 Tobit Bayesian Additive Regression Trees (TOBART-2) with Dirichlet Process Mixture distribution and soft trees.
result Corrects bias in treatment effect estimates by accounting for nonlinearities and model uncertainty.
Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, where there is also demand for probabilistic predictions with measures of uncertainty, the Bayesian additive regression trees (BART) model, i…
Proposes using external data to improve predictions in medical applications with limited samples.
problem Small sample sizes and complex covariate-response relationships in medical data.
method Integrates external co-data into Bayesian Additive Regression Trees (BART) using an empirical Bayes framework.
result Improves prediction accuracy compared to standard BART, especially for nonlinear relationships.
Probit Monotone BART estimates binary outcomes using monotonic functions.
problem Estimating conditional mean functions for binary outcomes with monotonicity constraints.
method Proposes a new BART variant that incorporates monotonicity constraints for binary outcomes.
result Allows for more precise estimation of monotonic functions in binary outcome models.
EPICSCORE improves conformal scores by explicitly accounting for epistemic uncertainty.
problem Overconfident predictions in data-sparse regions due to lack of epistemic uncertainty.
method Model-agnostic approach using Bayesian techniques like Gaussian Processes, Dropout, and Regression Trees.
result Enhanced predictive intervals that adaptively expand in sparse data regions and maintain compact intervals in abundant data.
Combines BART and Gaussian process for spatial covariate prediction with uncertainty.
problem Improving spatial prediction models with nonlinear and interaction covariates.
method Bayesian Additive Regression Trees (BART) combined with Gaussian process for spatial dependence.
result Effective in reducing computational burden through INLA and MCMC.
Extends Gaussian Process regression for handling multiple prior distributions.
problem Handling multiple prior distributions in Bayesian Machine Learning models.
method Mixtures of Gaussian Processes with analytical and Sparse Variational approaches.
result Effective in accounting for prior misspecification in functional regression problems.
BART and MOTR-BART improve tree-based predictions with local linear models.
problem Non-linearity and high-order interactions in data.
method Bayesian Additive Regression Trees (BART) and Model Trees BART (MOTR-BART) using piecewise linear functions.
result MOTR-BART achieves equal or better performance with fewer trees than BART.
This paper develops a novel stochastic tree ensemble method for nonlinear regression, which we refer to as XBART, short for Accelerated Bayesian Additive Regression Trees. By combining regularization and stochastic search strategies from Bayesian modeling with computationally efficient techniques from recursive partiti…
Bayesian econometrics improves nowcasting during pandemics.
problem Improving nowcasting during extreme economic events like pandemics.
method Bayesian econometric methods using non-parametric mixed frequency VARs with additive regression trees.
result Significant improvements in nowcasting performance compared to linear models.
Bayesian Topic Regression models causal inference with text and numerical data.
problem Causal inference using observational text data with both text and numerical confounders.
method Combines supervised Bayesian topic model with Bayesian regression framework, respecting the Frisch-Waugh-Lovell theorem.
result Joint approach recovers ground truth with lower bias than benchmarks, superior prediction results compared to separate approaches.
Functional BART adds shape priors to Bayesian tree regression for better curve fitting.
problem Regression with function-on-scalar data and shape constraints.
method Bayesian tree structure with spline representations, customized Bayesian backfitting algorithm, shape priors.
result Improved estimation and prediction accuracy with shape priors.
Bayesian Probabilistic Integration uses BART for high-dimensional, non-smooth functions.
problem Bayesian quadrature's limitations in high-dimensional or non-smooth functions.
method Bayesian Additive Regression Trees (BART) priors for numerical integration.
result Explicit convergence rates can be obtained in various settings.
Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger) data become available, facilitated by more powerful computing infrastructure. (2) Full probabilistic modeling rather than predicting just …
Paper introduces robust Gaussian process regression without sacrificing computational efficiency.
problem Violation of independent and identically distributed Gaussian observation noise assumption in Gaussian process regression.
method Proves robust and conjugate Gaussian process regression (RCGP) at no additional cost using generalised Bayesian inference.
result RCGP enables exact conjugate closed form updates in all settings where standard GPs admit them.
Bayesian method tackles variable selection in high-dimensional data.
problem Challenges in Bayesian variable selection with large P.
method Efficient MCMC scheme with sublinear cost per iteration, extended to generalized linear models.
result Demonstrated effectiveness on cancer and maize genomic data.
Additive nonparametric regression models provide an attractive tool for variable selection in high dimensions when the relationship between the response and predictors is complex. They offer greater flexibility compared to parametric non-linear regression models and better interpretability and scalability than the non-…
flexBART improves BART for categorical predictors by creating flexible tree partitions.
problem Limitation of BART in handling categorical predictors with one-hot encoding.
method flexBART re-implements BART with regression trees that can assign multiple levels to both branches of a decision tree node, and proposes a new decision rule prior for spatial data.
result flexBART often yields improved predictive performance and scales better to larger datasets than existing BART implementations.
Infinite BART model selects number of trees and allows different functions for clusters.
problem Regression and classification analysis with automatic tree selection and cluster-specific functions.
method Incorporates an Indian Buffet process prior to select a subset of decision trees for each observation.
result Infinite BART model outperforms classic BART on simulated and real datasets.
Bayesian additive regression trees (BART) (Chipman et. al., 2010) is a powerful predictive model that often outperforms alternative models at out-of-sample prediction. BART is especially well-suited to settings with unstructured predictor variables and substantial sources of unmeasured variation as is typical in the so…
Two methods use BART to model missing data in leaf photosynthetic trait data.
problem Handling missing data in multivariate outcomes with non-ignorable mechanisms.
method Bayesian Additive Regression Trees (BART) for joint modeling of data and missingness indicators.
result Both methods effectively recover various missingness mechanisms and outperform existing approaches.
Develops a new multivariate regression model for complex outcomes.
problem Flexible, heterogeneous, and residual-dependent multivariate regression problems.
method MultiVCBART framework with Graphical Horseshoe priors.
result Empirically outperforms existing models on sparse, high-dimensional datasets.
We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regression appr…
Develops a sparsity-inducing Bayesian Causal Forest for estimating heterogeneous treatment effects.
problem Estimating heterogeneous treatment effects using observational data with varying degrees of sparsity.
method Introduces a sparsity-inducing version of Bayesian Causal Forests with additional priors to adjust covariate weights.
result Improves adaptability to sparse data generating processes and uncovering moderating factors driving heterogeneity.