Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization

@article{Belakaria2021OutputSE,
  title={Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization},
  author={Syrine Belakaria and Aryan Deshwal and Janardhan Rao Doppa},
  journal={J. Artif. Intell. Res.},
  year={2021},
  volume={72},
  pages={667-715}
}
We consider the problem of black-box multi-objective optimization (MOO) using expensive function evaluations (also referred to as experiments), where the goal is to approximate the true Pareto set of solutions by minimizing the total resource cost of experiments. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive computational simulations. The key challenge is to select the sequence of experiments to… 

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