In recent years, solving optimization problems involving black-box simulators
has become a point of focus for the machine learning community due to their
ubiquity in science and engineering. The simulators describe a forward process
$f_{\mathrm{sim}}: (\psi, x) \rightarrow y$ from simulation parameters $\psi$
and input data $x$ to observations $y$, and the goal of the optimization
problem is to find parameters $\psi$ that minimize a desired loss function.
Sophisticated optimization algorithms typically require gradient information
regarding the forward process, $f_{\mathrm{sim}}$, with respect to the
parameters $\psi$. However, obtaining gradients from black-box simulators can
often be prohibitively expensive or, in some cases, impossible. Furthermore, in
many applications, practitioners aim to solve a set of related problems. Thus,
starting the optimization ``ab initio", i.e. from scratch, each time might be
inefficient if the forward model is expensive to evaluate. To address those
challenges, this paper introduces a novel method for solving classes of similar
black-box optimization problems by learning an active learning policy that
guides a differentiable surrogate's training and uses the surrogate's gradients
to optimize the simulation parameters with gradient descent. After training the
policy, downstream optimization of problems involving black-box simulators
requires up to $\sim$90\% fewer expensive simulator calls compared to baselines
such as local surrogate-based approaches, numerical optimization, and Bayesian
methods.

通过学习主动学习策略和使用代理模型的梯度从而使用梯度下降法优化模拟参数，本文引入了一种新的方法来解决类似黑盒优化问题的一类问题。经过训练后，相对于局部代理模型方法、数值优化和贝叶斯方法，基于黑盒模拟器的问题的下游优化仅需要约 90％较少的昂贵模拟调用。

模拟、快慢学习：学习黑箱优化的策略

Simulating, Fast and Slow: Learning Policies for Black-Box Optimization

Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying
equivalent entity pairs. While existing methods heavily rely on human-generated
labels, it is prohibitively expensive to incorporate cross-domain experts for
annotation in real-world scenarios. The advent of Large Language Models (LLMs)
presents new avenues for automating EA with annotations, inspired by their
comprehensive capability to process semantic information. However, it is
nontrivial to directly apply LLMs for EA since the annotation space in
real-world KGs is large. LLMs could also generate noisy labels that may mislead
the alignment. To this end, we propose a unified framework, LLM4EA, to
effectively leverage LLMs for EA. Specifically, we design a novel active
learning policy to significantly reduce the annotation space by prioritizing
the most valuable entities based on the entire inter-KG and intra-KG structure.
Moreover, we introduce an unsupervised label refiner to continuously enhance
label accuracy through in-depth probabilistic reasoning. We iteratively
optimize the policy based on the feedback from a base EA model. Extensive
experiments demonstrate the advantages of LLM4EA on four benchmark datasets in
terms of effectiveness, robustness, and efficiency. Codes are available via
this https URL

通过设计一种新颖的主动学习策略以及引入无监督标签优化方法，我们提出了一个统一的框架 LLM4EA，以有效地利用大型语言模型自动化实体对齐，并在四个基准数据集上进行了广泛实验，证明了其在效果、鲁棒性和效率方面的优势。