Mean-Variance Inference Under Gaussian Process for Robust Bayesian Optimization

This repository contains the code and experiments done in the work "Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty" by Jixiang Qing, Tom Dhaene and Ivo Couckuyt.

Installation

❗❗❗Caution: You are away from the main branch of Trieste, this branch contains certain other dependencies

install from sources, run

$ pip install -e.

in the repository root (tested with Python version 3.7.10).

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Tutorial Notebook

There is a tutorial notebook robust_optimization_considering_mean_variance.pct.py at (\docs\notebooks) demonstrating:

1. how to make use of the mean and variance inference.
2. how to use them for robust Bayesian Optimization.

In order to run the notebook, install the following dependency:

$ pip install -r notebooks/requirements.txt

Then, run the notebooks with

$ jupyter-notebook notebooks

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Reproduce the paper's result

If you'd like to reproduce the paper's result exactly, the following directories contain relevant experiments:

• docs\exp\FF_Variance\uncertainty_calibration Uncertainty Calibration
• docs\exp\FF_Variance\mc_comparison_of_input_and_spectral_density First Moment Comparison
• docs\exp\FF_Variance\robust_bayesian_optimization_exp RBO experiments
  • \scalar_mean_var_exp
  • \var_as_con_acq_exp
  • \mo_mean_var_exp

Note: some scripts containing plot labels that depends on a local LaTeX compiler.

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Citation

If you find this work or repository helpful, please kindly consider citing our work:

@inproceedings{qing2022spectral,
  title={Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty},
  author={Qing, Jixiang and Dhaene, Tom and Couckuyt, Ivo},
  booktitle={International Conference on Machine Learning},
  pages={18096--18121},
  year={2022},
  organization={PMLR}
}