The acquisition function, a critical component in Bayesian optimization (BO),
can often be written as the expectation of a utility function under a surrogate
model. However, to ensure that acquisition functions are tractable to optimize,
restrictions must be placed on the surrogate model and utility function. To
extend BO to a broader class of models and utilities, we propose
likelihood-free BO (LFBO), an approach based on likelihood-free inference. LFBO
directly models the acquisition function without having to separately perform
inference with a probabilistic surrogate model. We show that computing the
acquisition function in LFBO can be reduced to optimizing a weighted
classification problem, where the weights correspond to the utility being
chosen. By choosing the utility function for expected improvement (EI), LFBO
outperforms various state-of-the-art black-box optimization methods on several
real-world optimization problems. LFBO can also effectively leverage composite
structures of the objective function, which further improves its regret by
several orders of magnitude.

提出了一种基于无似然推断的方法（LFBO），通过直接建模采集功能而不必单独执行概率替代模型上的推断，使 BO 扩展到更广泛的模型和实用程序类别，通过适当地选择实用程序函数（EI），LFBO 在几个实际最优化问题上优于各种最先进的黑匣子优化方法。

无似然贝叶斯优化的通用配方

A General Recipe for Likelihood-free Bayesian Optimization

Individualized treatment rules aim to identify if, when, which, and to whom
treatment should be applied. A globally aging population, rising healthcare
costs, and increased access to patient-level data have created an urgent need
for high-quality estimators of individualized treatment rules that can be
applied to observational data. A recent and promising line of research for
estimating individualized treatment rules recasts the problem of estimating an
optimal treatment rule as a weighted classification problem. We consider a
class of estimators for optimal treatment rules that are analogous to convex
large-margin classifiers. The proposed class applies to observational data and
is doubly-robust in the sense that correct specification of either a propensity
or outcome model leads to consistent estimation of the optimal individualized
treatment rule. Using techniques from semiparametric efficiency theory, we
derive rates of convergence for the proposed estimators and use these rates to
characterize the bias-variance trade-off for estimating individualized
treatment rules with classification-based methods. Simulation experiments
informed by these results demonstrate that it is possible to construct new
estimators within the proposed framework that significantly outperform existing
ones. We illustrate the proposed methods using data from a labor training
program and a study of inflammatory bowel syndrome.

本文介绍了通过重新定义分类问题，将最优治疗规则的估计转化为分类问题，提出了一类新的基于凸大间隔分类器的最优治疗规则估计方法，这类方法具有双重稳健性，能够在使用属性模型或结果模型进行正确说明时一致地估计最优个体化治疗规则，并使用半参数效率理论技术推导了提议的估计器的一致收敛率，并使用这些结果表征了基于分类的方法估计最优治疗规则时的偏差 - 方差折衷，并通过仿真实验证明了在提议的框架内构造的新估计器可以显著优于现有估计器，数据来自劳动培训计划和炎症性肠病研究。