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The paper introduces a few-shot design optimization setting where high-dimensional auxiliary information $h(x)$ is generated alongside the performance measure $f(x)$ and a history of related tasks is available. They propose a neural model that predicts $f(x)$ for new designs using few-shot context containing observations of $h(x)$, effectively leveraging auxiliary feedback. Experiments on robotic hardware design and neural network hyperparameter tuning demonstrate that the method achieves more accurate few-shot prediction and faster optimization compared to multi-task optimization baselines.
Achieve faster design optimization by exploiting auxiliary information generated during experiments, significantly outperforming multi-task baselines in robotic hardware design and neural network hyperparameter tuning.
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.