用元学习预训练低维空间，加速黑箱优化寻优
Learning Low-Dimensional Embeddings for Black-Box Optimization
- 通过元学习构建特定问题类的低维流形
- 在新问题上于低维空间优化，显著降低试错成本
- 适合高维、试次受限的黑箱优化场景
当基于梯度的方法不可行时，黑箱优化（BBO）提供了一种有效替代方案。然而，传统BBO在高维问题和有限试验预算下往往表现不佳。本文提出一种基于元学习的新方法，预先计算特定优化问题类中最优解所处的低维流形。当面对从该类中采样的新问题实例时，可在低维空间中执行黑箱优化，从而大幅减少逼近最优解所需的努力。
原文摘要 · Abstract (English)
When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited trial budgets. In this work, we propose a novel approach based on meta-learning to pre-compute a reduced-dimensional manifold where optimal points lie for a specific class of optimization problems. When optimizing a new problem instance sampled from the class, black-box optimization is carried out in the reduced-dimensional space, effectively reducing the effort required for finding near-optimal solutions.
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