用变分自编码器学习最优核函数，大幅降低黑箱优化采样成本。
Kernel Learning for Sample Constrained Black-Box Optimization
- 在变分自编码器隐空间构建连续核函数空间，自动搜索最优核
- 在合成函数和真实应用中均显著降低采样次数达成最优解
- 适合需要少样本交互的个性化系统，如助听器调优、生成模型优化
黑箱优化（BBO）旨在高维空间中优化未知函数。许多实际应用中，函数采样代价高昂，需严格控制采样预算。当前研究通过学习函数形状/结构（即核学习）来减少采样需求。本文提出一种新方法，用于学习高斯过程的核函数：在变分自编码器的隐空间中构建连续核空间，并通过辅助优化寻找最佳核。结果表明，所提方法——核优化黑箱优化（KOBO），在合成基准函数和真实应用场景中均能以更少的采样预算逼近最优解。例如，在助听器个性化过程中可减少对用户的音频查询次数，或在用户仅提供有限评分时使生成模型快速收敛至理想图像。
原文摘要 · Abstract (English)
Black box optimization (BBO) focuses on optimizing unknown functions in high-dimensional spaces. In many applications, sampling the unknown function is expensive, imposing a tight sample budget. Ongoing work is making progress on reducing the sample budget by learning the shape/structure of the function, known as kernel learning. We propose a new method to learn the kernel of a Gaussian Process. Our idea is to create a continuous kernel space in the latent space of a variational autoencoder, and run an auxiliary optimization to identify the best kernel. Results show that the proposed method, Kernel Optimized Blackbox Optimization (KOBO), outperforms state of the art by estimating the optimal at considerably lower sample budgets. Results hold not only across synthetic benchmark functions but also in real applications. We show that a hearing aid may be personalized with fewer audio queries to the user, or a generative model could converge to desirable images from limited user ratings.
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