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 [1].
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. |