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..
On Semi-parametric Inference for BART
Authors: Veronika Rockova
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we continue the theoretical investigation of BART initiated recently by (Roˇckov a and van der Pas, 2017). We focus on statistical inference questions. In particular, we study the Bernstein-von Mises (Bv M) phenomenon (i.e. asymptotic normality) for smooth linear functionals of the regression surface within the framework of nonparametric regression with fixed covariates. Our semi-parametric Bv M results show that, beyond rate-optimal estimation, BART can be also used for valid statistical inference. |
| Researcher Affiliation | Academia | 1Booth School of Business, University of Chicago. Correspondence to: Veronika Roˇckov a <Veronika.Rockova@Chicago Booth.edu>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes a 'standard non-parametric regression setup' with 'fixed (rescaled) predictors' but does not mention the use of a specific, publicly available dataset (e.g., by name, citation, or link). |
| Dataset Splits | No | The paper is theoretical and focuses on statistical inference properties. It does not describe an experimental setup involving training, validation, and test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not detail any software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |