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..
Efficiently sampling functions from Gaussian process posteriors
Authors: James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a series of experiments designed to test competing sampling schemes statistical properties and practical ramifications, we demonstrate how decoupled sample paths accurately represent Gaussian process posteriors at a fraction of the usual cost. |
| Researcher Affiliation | Academia | 1Imperial College London 2St. Petersburg State University 3St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences 4University College London. |
| Pseudocode | No | The paper does not contain explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | 4Code: https://github.com/j-wilson/GPflowSampling |
| Open Datasets | No | The paper mentions varying amounts of "training data n" and functions drawn from "known GP priors" but does not specify a named public dataset or provide access information for a training dataset. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits, only mentioning training and test locations. |
| Hardware Specification | No | The paper does not explicitly mention specific hardware specifications such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Across trials, we varied both the dimensionality d of search spaces X = [0, 1]d and the number of initial basis functions ℓ. We set κ = d, but this choice was not found to greatly influence results. The total number of basis functions allocated to weight-space and decoupled samplers was again matched, so that b = m + ℓ. [...] Results using ℓ {1024, 4096, 16384} initial bases correspond with {light, medium, dark} tones and { , , } markers. |