We provide the implementation for our algorithm Gaussian Process Optimistic Optimisation (GPOO) and related algorithms in supplementary_code. Full paper (including appendix) can be found here.
In terminal:
conda env create -f environment.yml
conda activate gpoo_env
python run_sim.py -h
This will gives
Run Simulation for GPOO project.
positional arguments:
opt_num choose what f to use, choices: 1,2,3
optional arguments:
-h, --help show this help message and exit
--n N budget (should be positive integer)
--r R number of repeat (should be positive integer)
--alg [ALG [ALG ...]]
please list all algorithms to run. Choices: StoOO, GPOO, GPTree, SK
Here is an example of usage: to run GPOO algorithm with function choice 1, with budget 80, 30 independent runs:
python run_sim.py 1 --n 80 --r 30 --alg GPOO
Cite us by
@article{zhang2022GPOO,
title={Gaussian Process Bandits with Aggregated Feedback},
author={Zhang, Mengyan and Tsuchida, Russell and Ong, Cheng Soon},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2022}
}