利用辅助信息实现少样本高效设计优化，提升复杂任务求解速度。
Few-Shot Design Optimization by Exploiting Auxiliary Information
- 通过神经模型结合少量上下文中的辅助信息预测新设计性能
- 在机器人硬件与神经网络调参任务中显著加速优化过程
- 适合需快速迭代的工程设计与药物发现等场景
许多真实世界的设计问题涉及优化昂贵的黑箱函数 $f(x)$，如硬件设计或药物发现。贝叶斯优化因其样本效率成为主流方法。然而，现有方法忽略实际实验中产生的丰富辅助信息。本文提出新设置：实验不仅输出性能 $f(x)$，还生成高维辅助信息 $h(x)$，并可利用同一任务族的历史任务加速优化。核心挑战在于如何有效表示与利用 $h(x)$ 来解决新任务。我们提出一种基于神经模型的新方法，仅用少量上下文即可预测未见设计的 $f(x)$。在机器人硬件设计与神经网络超参数调优两个挑战性领域进行评估，并引入新的设计问题与大规模基准。结果表明，该方法能有效利用辅助信息，实现更准确的少样本预测与更快的优化，显著优于多种多任务优化方法。
原文摘要 · Abstract (English)
Many real-world design problems involve optimizing an expensive black-box function $f(x)$, such as hardware design or drug discovery. Bayesian Optimization has emerged as a sample-efficient framework for this problem. However, the basic setting considered by these methods is simplified compared to real-world experimental setups, where experiments often generate a wealth of useful information. We introduce a new setting where an experiment generates high-dimensional auxiliary information $h(x)$ along with the performance measure $f(x)$; moreover, a history of previously solved tasks from the same task family is available for accelerating optimization. A key challenge of our setting is learning how to represent and utilize $h(x)$ for efficiently solving new optimization tasks beyond the task history. We develop a novel approach for this setting based on a neural model which predicts $f(x)$ for unseen designs given a few-shot context containing observations of $h(x)$. We evaluate our method on two challenging domains, robotic hardware design and neural network hyperparameter tuning, and introduce a novel design problem and large-scale benchmark for the former. On both domains, our method utilizes auxiliary feedback effectively to achieve more accurate few-shot prediction and faster optimization of design tasks, significantly outperforming several methods for multi-task optimization.
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