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..
Geometric convergence of elliptical slice sampling
Authors: Viacheslav Natarovskii, Daniel Rudolf, Björn Sprungk
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our result for Gaussian posteriors as they appear in Gaussian process regression, as well as in a setting of a multi-modal distribution. Remarkably, our numerical experiments indicate a dimension-independent performance of elliptical slice sampling even in situations where our ergodicity result does not apply. |
| Researcher Affiliation | Academia | 1Institute for Mathematical Stochastics, Georg-August Universit at G ottingen, G ottingen, Germany 2Faculty of Mathematics and Computer Science, Technische Universit at Bergakademie Freiberg, Germany. |
| Pseudocode | Yes | Algorithm 1 Elliptical Slice Sampler |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper discusses 'Gaussian process regression' and 'logistic regression' with data, but does not provide specific access information (link, DOI, citation) for any publicly available dataset used in its experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For each algorithm we set the initial state to be 0 Rd and compute the ESS for f(x) := log(1 + x ), n0 := 105 and n := 106. Both Metropolis algorithms (the RWM and the p CN Metropolis) were tuned to an averaged acceptance probability of approximately 0.25. |