# 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}
}
• Published 13 October 2021
• Computer Science
• J. Artif. Intell. Res.
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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## References

SHOWING 1-10 OF 71 REFERENCES

• Computer Science
NeurIPS
• 2019
This work proposes a novel approach referred to as Max-value Entropy Search for Multi-objective Optimization (MESMO), which employs an output-space entropy based acquisition function to efficiently select the sequence of inputs for evaluation for quickly uncovering high-quality solutions.
• Computer Science
AAAI
• 2020
Experiments show that MF-OSEMO, with both approximations, significantly improves over the state-of-the-art single-fidelity algorithms for multi-objective optimization.
• Computer Science
AAAI
• 2020
This work proposes a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation to solve the problem of multi-objective (MO) blackbox optimization using expensive function evaluations.
• Computer Science
ArXiv
• 2020
A Bayesian optimization method that can be used to solve constrained multi-objective problems when the objectives and the constraints are expensive to evaluate, and its execution time is smaller than other information-based methods.
• Computer Science
ICML
• 2020
This work proposes a novel information theoretic approach to multi-fidelity Bayesian optimization (MFBO) based on a variant of information-based BO called max-value entropy search (MES), which greatly facilitates evaluation of the information gain in MFBO.
Results show that NSGA-II, a popular multiobjective evolutionary algorithm, performs well compared with random search, even within the restricted number of evaluations used.
• Computer Science
NIPS
• 2014
This work proposes a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES), which codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution.
• Computer Science
PPSN
• 2008
This paper provides a review of contemporary multiobjective approaches based on the singleobjective meta-model-assisted 'Efficient Global Optimization' (EGO) procedure and describes their main concepts and introduces a new EGO-based MOOA, which utilizes the $\mathcal{S}$-metric or hypervolume contribution to decide which solution is evaluated next.
• Computer Science
ECML/PKDD
• 2020
A novel multi-task version of entropy search is derived, delivering robust performance with low computational overheads across classic optimization challenges and multi- task hyper-parameter tuning.