belakaria / USEMOC

Python implementation for USeMOC paper "Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization with Constraints".

Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization with Constraints

This repository contains the python implementation for USeMOC from the ICML 2020 Workshop on Automated Machine Learning (AutoML) "Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization with Constraints".

The implementation handles automatically the batch version of the algorithm by setting the variable "batch_size" to a number higher than 1.

Requirements

The code is implemented in Python and requires the following packages:

1. platypus

2. sklearn.gaussian_process

3. pygmo

Citation

If you use this code please cite our paper:

@article{belakaria2020uncertainty,
  title={Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints},
  author={Belakaria, Syrine and Deshwal, Aryan and Doppa, Janardhan Rao},
  journal={Workshop on Automated Machine Learning (AutoML), ICML},
  year={2020}
}