Model-based sequential approaches to discrete "black-box" optimization,
including Bayesian optimization techniques, often access the same points
multiple times for a given objective function in interest, resulting in many
steps to find the global optimum. Here, we numerically study the effect of a
postprocessing method on Bayesian optimization that strictly prohibits
duplicated samples in the dataset. We find the postprocessing method
significantly reduces the number of sequential steps to find the global
optimum, especially when the acquisition function is of maximum a posterior
estimation. Our results provide a simple but general strategy to solve the slow
convergence of Bayesian optimization for high-dimensional problems.

本文研究了一个后处理方法对贝叶斯优化的影响，该方法禁止数据集中存在重复样本，结果发现后处理方法显著降低了贝叶斯优化寻找全局最优解所需的顺序步骤的数量，特别是当求解函数采用最大后验估计的收益函数时，这为解决高维问题下贝叶斯优化的收敛速度慢提供了一种简单而普适的策略。

组合贝叶斯优化的随机后处理

Random postprocessing for combinatorial Bayesian optimization

Seven years ago, researchers proposed a postprocessing method to equalize the
error rates of a model across different demographic groups. The work launched
hundreds of papers purporting to improve over the postprocessing baseline. We
empirically evaluate these claims through thousands of model evaluations on
several tabular datasets. We find that the fairness-accuracy Pareto frontier
achieved by postprocessing contains all other methods we were feasibly able to
evaluate. In doing so, we address two common methodological errors that have
confounded previous observations. One relates to the comparison of methods with
different unconstrained base models. The other concerns methods achieving
different levels of constraint relaxation. At the heart of our study is a
simple idea we call unprocessing that roughly corresponds to the inverse of
postprocessing. Unprocessing allows for a direct comparison of methods using
different underlying models and levels of relaxation. Interpreting our
findings, we recall a widely overlooked theoretical argument, present seven
years ago, that accurately predicted what we observe.

本文通过数千次模型评估，并采用一种叫做 “取消后处理方法” 的简单理念，对多篇针对改进后处理基线的论文进行实证评估，发现在不同的表格数据集上所实现的公平性 - 准确性 Pareto 前沿线包含了所有现有的其他改进方法。作者还纠正了之前的两个常见方法学错误。