SOBER

Fast Bayesian optimization, quadrature, inference over arbitrary domain with GPU parallel acceleration based on GPytorch and BoTorch. The paper is here arXiv,

Red star: ground truth
black crosses: next batch queries recommended by SOBER
white dots: historical observations
Branin function: blackbox function to maximise
$\pi$: the probability of global optimum locations estimated by SOBER

Features

fast batch Bayesian optimization
fast batch Bayesian quadrature
fast Bayesian inference
fast fully Bayesian Gaussian process modelling and related acquisition functions
sample-efficient simulation-based inference
GPU acceleration
Arbitrary domain space (continuous, discrete, mixture, or domain space as dataset)
Arbitrary kernel for surrogate modelling
Arbitrary acquisition function
Arbitrary prior distribution for Bayesian inference

Tutorials for practitioners/researchers

We prepared the detailed explanations about how to customize SOBER for your tasks.
See tutorials.

01 How does SOBER work?
02 Customise prior for various domain type
03 Customise acquisition function
04 Fast fully Bayesian Gaussian process modelling
05 Fast Bayesian inference for simulation-based inference
06 Tips for drug discovery

Examples

See examples for reproducing the results in the paper.

Brief explanation

We solve batch global optimization as Bayesian quadrature;
We select the batch query locations to minimize the integration error of the true function $f_\text{true}$ over the probability measure $\pi$. $\pi$ is the probability of global optimum locations estimated by SOBER, and becomes confident (shrink toward true global optima) over iterations.

Requirements

PyTorch
GPyTorch
BoTorch

Cite as

Please cite this work as

@article{adachi2023sober,
  title={SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces},
  author={Adachi, Masaki and Hayakawa, Satoshi and Hamid, Saad and Jørgensen, Martin and Oberhauser, Harald and Osborne, Michael A.},
  journal={arXiv preprint arXiv:2301.11832},
  year={2023}
}