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..
Gibbsian Polar Slice Sampling
Authors: Philip Schär, Michael Habeck, Daniel Rudolf
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments in a variety of settings indicate that our proposed algorithm outperforms the two most closely related approaches, elliptical slice sampling (Murray et al., 2010) and hit-and-run uniform slice sampling (Mac Kay, 2003). |
| Researcher Affiliation | Academia | 1Microscopic Image Analysis Group, Friedrich Schiller University Jena, Jena, Germany 2Faculty of Computer Science and Mathematics, University of Passau, Passau, Germany. |
| Pseudocode | Yes | Algorithm 1 Gibbsian Polar Slice Sampling; Algorithm 2 Geodesic Shrinkage; Algorithm 3 Radius Shrinkage |
| Open Source Code | Yes | Source code allowing the reproduction (in nature) of our experimental results is provided in a github repository6. (Footnote 6: https://github.com/microscopic-imageanalysis/Gibbsian Polar Slice Sampling) |
| Open Datasets | Yes | We consider the Cover Type data set (Blackard, 1998; Blackard et al.) |
| Dataset Splits | Yes | We use only 10% of it as training data and the remaining 90% as test data. |
| Hardware Specification | Yes | All of our experiments were conducted on a workstation equipped with an AMD Ryzen 5 PRO 4650G CPU. |
| Software Dependencies | Yes | an easily usable, general purpose implementation of GPSS in Python 3.10, based on numpy. |
| Experiment Setup | Yes | We chose the sample space dimension to be d = 100, initialized all samplers with x0 := (1, . . . , 1)T and ran each of them for N = 106 iterations. |