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..
BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain
Authors: Zhao Tang Luo, Huiyan Sang, Bani Mallick
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the model performance by simulation experiments and a real chlorophyll data set in Aral Sea. |
| Researcher Affiliation | Academia | Zhao Tang Luo Department of Statistics Texas A&M University EMAIL Huiyan Sang Department of Statistics Texas A&M University EMAIL Bani Mallick Department of Statistics Texas A&M University EMAIL |
| Pseudocode | No | The paper describes algorithms and steps for Bayesian inference in prose, but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of BAST is available at https://github.com/ztluostat/BAST. |
| Open Datasets | Yes | We apply BAST to analyze average remote sensed chlorophyll data in the Aral data over 1998-2002, which are available in the R package gamair [42]. |
| Dataset Splits | Yes | We first compare the prediction performance of all the models via 10-fold cross-validation. |
| Hardware Specification | Yes | All experiments are conducted on a single CPU core (Intel Xeon E5-2630 v4 CPU @ 2.20GHz) with 10GB of memory. |
| Software Dependencies | No | The paper mentions using the "R package gamair [42]" for a dataset, but it does not provide specific version numbers for the primary software components used to implement and run BAST (e.g., Python, PyTorch, TensorFlow, specific libraries). |
| Experiment Setup | Yes | We use M = 20 weak learners and set λk = 4 and k = 10 to restrict the size of each partition. ... The probabilities for MCMC moves are set as rb = rd = rc = 0.3 and rh = 0.1, with adjustments for cases where km = 1 or k. We run the MCMC for both BAST and BART for 20, 000 iterations, discarding the first half and retaining samples every 5 iterations. |