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..
Posterior and Computational Uncertainty in Gaussian Processes
Authors: Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate the consequences of ignoring computational uncertainty and show how implicitly modeling it improves generalization performance on benchmark datasets. |
| Researcher Affiliation | Academia | 1 University of Tübingen 2 Columbia University 3 Max Planck Institute for Intelligent Systems, Tübingen |
| Pseudocode | Yes | Algorithm 1: A Class of Computation-Aware Iterative Methods for GP Approximation |
| Open Source Code | Yes | An implementation of Algorithm 1, based on Ke Ops [48] and Prob Num [60], is available at: https://github.com/JonathanWenger/itergp |
| Open Datasets | Yes | as well as a range of UCI datasets [61] with training set sizes n = 5, 287 to 57, 247, dimensions d = 9 to 26 and standardized features. |
| Dataset Splits | Yes | All experiments were run 10 times with randomly sampled training and test splits of 90/10 and we report average metrics with 95% confidence intervals. |
| Hardware Specification | Yes | All experiments were run on an NVIDIA GeForce RTX 2080 Ti graphics card. |
| Software Dependencies | No | An implementation of Algorithm 1, based on Ke Ops [48] and Prob Num [60], is available at: https://github.com/JonathanWenger/itergp. While specific software names are mentioned, their version numbers are not provided. |
| Experiment Setup | No | The paper states that hyperparameters are selected using a specific training procedure and that a zero mean prior and Matérn(1/2) kernel are used. However, it does not provide specific numerical values for hyperparameters such as learning rate, batch size, or optimizer settings within the main text. |