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..
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Authors: Theofanis Karaletsos, Thang D. Bui
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the proposed priors and inference scheme on several regression and classification datasets, and study effects of per-datapoint priors as we proposed on extrapolation, interpolation, and out-of-distribution data. |
| Researcher Affiliation | Collaboration | Theofanis Karaletsos Facebook EMAIL Thang D. Bui Uber AI and University of Sydney EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states 'Additional updates and results will be available on https://arxiv.org/abs/2002.04033.' which is a link to the arXiv paper itself, not source code. |
| Open Datasets | Yes | We first illustrate the performance of the proposed model on a classification example. We generate a dataset of 100 data points and four classes |
| Dataset Splits | No | The paper mentions 'training points' and 'test sets' but does not specify explicit train/validation/test dataset splits with percentages, sample counts, or clear methodologies for reproduction. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU/CPU models or memory details. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use M = 50 inducing weights for all experiments in this section. Details for the experimental settings and additional experiments are included in the appendices. |