Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Oblique Bayesian Additive Regression Trees
Authors: Paul-Hieu V. Nguyen, Ryan Yee, Sameer Deshpande
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using several synthetic and real-world benchmark datasets, we systematically compared our oblique BART implementation to axis-aligned BART and other tree ensemble methods, finding that oblique BART was competitive with and sometimes much better than those methods. |
| Researcher Affiliation | Academia | Paul-Hieu V. Nguyen EMAIL Department of Statistics University of Wisconsin Madison; Ryan Yee EMAIL Department of Statistics University of Wisconsin Madison; Sameer K. Deshpande EMAIL Department of Statistics University of Wisconsin Madison |
| Pseudocode | No | The paper describes the algorithms and methods textually and with diagrams (Figure 3 for grow/prune moves), but it does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks formatted as code. |
| Open Source Code | Yes | An R package implementing oblique BART is available at https://github.com/paulhnguyen/obliqueBART. |
| Open Datasets | Yes | We obtained most of these datasets from UCI Machine Learning Repository (https://archive.ics.uci.edu); the Journal of Applied Econometrics data archive (http://qed.econ.queensu.ca/jae/); and from several R packages. See Table A1 for the dimensions of and links to these datasets. |
| Dataset Splits | Yes | We created 20 random 75%-25% training-testing splits of each dataset. |
| Hardware Specification | No | We performed all of our experiments on a shared high-throughput computing cluster. This statement is too general and does not provide specific hardware details like GPU/CPU models or memory. |
| Software Dependencies | Yes | We fit BART, RF, ERT, and XGBoost models using implementations available in the R packages BART (Sparapani et al., 2021), randomForest (Liaw & Wiener, 2002), ranger (Wright & Ziegler, 2017), and xgboost (Chen et al., 2024). We additionally note the use of ALGLIB 4.01.0 (Bochkanov, 2023). |
| Experiment Setup | Yes | For oblique BART and BART, we compute posterior means of f(x) (for regression) and P(y = 1|x) (for classification) based on 1000 samples obtained by simulating a single Markov chain for 2000 iterations and discarding the first 1000 as burn-in. For each competing method, we tuned hyperparameters using 5-fold cross-validation on each training dataset. Table A2 shows the grids of values considered for each method s hyperparameters. |