AIRBO

AIRBO: Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs

Welcome to the repository for our paper: Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs, NeurIPS 2023.

Overview

Bayesian optimization is a powerful framework for global optimization of expensive black-box functions. However, most existing methods assume that the inputs are perfectly known and fixed, which is often not the case in real-world applications. In AIRBO, we propose a novel method for efficient robust Bayesian optimization that can handle arbitrary input uncertainties. In particular, we design an MMD-based kernel to measure uncertain inputs in an RKHS and employ Nystrom approximation to boost the inference of GP posterior.

Features

• Can take random variables (e.g., input with noise) as inputs and propagate the input uncertainty to the posterior.
• Can be applied to arbitrary input distribution if we can sample from it.
• Aim to find a robust optimum whose expected function value is best under the input uncertainty.
• Efficient posterior inference (40 times more efficient than the integral kernel).

Requirements

• Python 3.8 or higher

• Botorch 0.9.2 or higher

• GPytorch 1.11 or higher

*see more package dependencies in the requirements.txt

Usage

To use our implementation, follow these steps:

1. Clone the repository

2. Install the required packages:

   pip install -r requirements.txt

3. To compare the modeling performance of robust surrogate models, run:

   sh scripts/compare_surrogate_models.sh

4. To compare the optimization on benchmark functions:

   sh scripts/compare_robust_optimization.sh

5. Run robust optimization in a push-world game (need Box2D and pygame packages)

   sh scripts/compare_optimization_in_push_world.sh

Citation

If you find this work useful, please cite our paper: Yang, Lin, et al. "Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs." NeurIPS 2023.

@inproceedings{yang2023efficient,
  title={Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs},
  author={Yang, Lin and Lyu, Junlong and Lyu, Wenlong and Chen, Zhitang},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}