ICML 2022 | meta proposes a robust multi-objective Bayesian optimization method to effectively deal with input noise

Thesis link ：

https://arxiv.org/abs/2202.07549

Project links ：

https://github.com/facebookresearch/robust_mobo

This article is about facebook Published in ICML 2022 A piece of work for , It theoretically analyzes the multi-objective Bayesian Optimization with input noise .

This paper deals with the input noise problem of multi-objective optimization , Combined with Bayesian Optimization and Pareto optimization, the global multi-objective VaR is designed and optimized , To solve the problem of black box constraint sensitive to input noise . Bayesian optimization by adjusting design parameters , Black box performance indicators with high evaluation cost can be optimized . Although many methods have been proposed to optimize a single target under input noise , However, there is still a lack of methods to solve the practical problem that multiple targets are sensitive to input disturbances .

In this work , The author proposes the first robust multi-objective Bayesian optimization method to deal with input noise . The author formalizes the goal as a risk measure to optimize an uncertain goal , That is, multivariable value at risk （MVaR）. Due to direct optimization MVaR In many cases, it is computationally infeasible , The author proposes an extensible 、 A theory based approach to use random scales to optimize MVaR. Experimentally speaking , This method is significantly better than other methods in data set , And effectively realize the optimal Luban design .