Many real world applications can be framed as multi-objective optimization
problems, where we wish to simultaneously optimize for multiple criteria.
Bayesian optimization techniques for the multi-objective setting are pertinent
when the evaluation of the functions in question are expensive. Traditional
methods for multi-objective optimization, both Bayesian and otherwise, are
aimed at recovering the Pareto front of these objectives. However, in certain
cases a practitioner might desire to identify Pareto optimal points only in a
subset of the Pareto front due to external considerations. In this work, we
propose a strategy based on random scalarizations of the objectives that
addresses this problem. Our approach is able to flexibly sample from desired
regions of the Pareto front and, computationally, is considerably cheaper than
most approaches for MOO. We also study a notion of regret in the
multi-objective setting and show that our strategy achieves sublinear regret.
We experiment with both synthetic and real-life problems, and demonstrate
superior performance of our proposed algorithm in terms of the flexibility and
regret.

本文提出了一种基于随机标量化策略的多目标优化方法，可快速、灵活地从 Pareto 前沿的特定区域中采样，且在多项真实问题和合成问题的实验中显示了良好表现。