Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
Authors: Julian Berk, Sunil Gupta, Santu Rana, Svetha Venkatesh
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {jmberk, sunil.gupta, santu.rana, svetha.venkatesh}@deakin.edu.au |
| 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. |