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..
Scalable Bayesian Rule Lists
Authors: Hongyu Yang, Cynthia Rudin, Margo Seltzer
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through a series of controlled experiments, we show that SBRL is over two orders of magnitude faster than the previous best code for this problem. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology, Cambridge, Massachusetts, USA 2Duke University, Durham, North Carolina, USA 3Harvard University, Cambridge, Massachusetts, USA. |
| Pseudocode | Yes | Algorithm 1 Calculating bj s |
| Open Source Code | Yes | Code for SBRL is available at the following link: https://github.com/Hongyuy/sbrlmod Link to R package SBRL on CRAN: https://cran.r-project.org/web/packages/sbrl/ index.html |
| Open Datasets | Yes | We benchmark using publicly available datasets (see Bache & Lichman, 2013) |
| Dataset Splits | Yes | Evaluations of prediction quality, sparsity, and timing were done using 10-fold cross validation. |
| Hardware Specification | No | The paper mentions that experiments were run 'on a laptop' but does not provide specific hardware details such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions 'python implementation', 'python gmpy library', 'Python to C', and 'GMP library' but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | The prior parameters were fixed at η = 1, and α = (1, 1). |