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Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Requirements

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

  1. sobol_seq

  2. platypus

  3. sklearn.gaussian_process

Citation

If you use this code please cite our papers:

@article{belakaria2021output,
  title={Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization},
  author={Belakaria, Syrine and Deshwal, Aryan and Doppa, Janardhan Rao},
  journal={Journal of Artificial Intelligence Research},
  volume={72},
  pages={667-715},
  year={2021}
}

@article{belakaria2020information,
  title={Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations},
  author={Belakaria, Syrine and Deshwal, Aryan and Doppa, Janardhan Rao},
  journal={Workshop on machine learning for engineering modeling, simulation and design (NeurIPS)},
  year={2020}
}

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