Data-driven black-box model-based optimization (MBO) problems arise in a
great number of practical application scenarios, where the goal is to find a
design over the whole space maximizing a black-box target function based on a
static offline dataset. In this work, we consider a more general but
challenging MBO setting, named constrained MBO (CoMBO), where only part of the
design space can be optimized while the rest is constrained by the environment.
A new challenge arising from CoMBO is that most observed designs that satisfy
the constraints are mediocre in evaluation. Therefore, we focus on optimizing
these mediocre designs in the offline dataset while maintaining the given
constraints rather than further boosting the best observed design in the
traditional MBO setting. We propose retrieval-enhanced offline model-based
optimization (ROMO), a new derivable forward approach that retrieves the
offline dataset and aggregates relevant samples to provide a trusted
prediction, and use it for gradient-based optimization. ROMO is simple to
implement and outperforms state-of-the-art approaches in the CoMBO setting.
Empirically, we conduct experiments on a synthetic Hartmann (3D) function
dataset, an industrial CIO dataset, and a suite of modified tasks in the
Design-Bench benchmark. Results show that ROMO performs well in a wide range of
constrained optimization tasks.

在这项工作中，我们考虑了一种更加普遍但具有挑战性的约束模型优化问题（CoMBO），其中只有部分设计空间可以进行优化，而其余部分受到环境的约束。我们提出了一种新的推理增强的离线模型优化方法（ROMO），它通过检索离线数据集并聚合相关样本来提供可信的预测，然后在此基础上进行梯度优化。ROMO 在 CoMBO 设置中表现出色，优于最先进的方法。