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..
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
Authors: Julian Berk, Sunil Gupta, Santu Rana, Svetha Venkatesh
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present results that demonstrate the performance of RGP-UCB in comparison to other common acquisition functions. We also demonstrate the impact of varying the θ parameter of the gamma distribution used to sample βt. The Python code used for this paper can be found at https://github.com/jmaberk/RGPUCB. We test our method against a selection of common acquisition functions on a range of Bayesian optimisations problems. These include a range of synthetic benchmark functions and real-world optimisation problems. These are all transformed into continuous maximisation problems for consistency. |
| Researcher Affiliation | Academia | Julian Berk , Sunil Gupta , Santu Rana and Svetha Venkatesh Applied Artificial Intelligence Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 Bayesian Optimisation with RGP-UCB |
| Open Source Code | Yes | The Python code used for this paper can be found at https://github.com/jmaberk/RGPUCB. |
| Open Datasets | Yes | All benchmark functions use the recommended parameters from https://www.sfu.ca/ ssurjano/optimization.html and All experiments are done with the public Space GA scale dataset 2. Dataset can be found at https://www.csie.ntu.edu.tw cjlin/ libsvmtools/datasets/regression.html |
| Dataset Splits | No | The paper does not provide explicit training/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Python code' but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In each case, the experiment was run for 40d iterations and repeated 10 times with 3d+1 different initial points. The initial points are chosen randomly with a Latin hypercube sample scheme [Jones, 2001]. and We also demonstrate the impact of varying the θ parameter of the gamma distribution used to sample βt. |