用仿真实例迁移训练随机森林代理模型，降低真实场景建模数据与计算成本。
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks
- 通过未知仿射变换映射源域与目标域，实现非可微代理模型的迁移。
- 在BBOB基准和4个真实任务上，仅需少量目标数据即显著提升模型性能。
- 适合缺乏标注数据的复杂现实问题，如工程优化与科学计算场景。
代理模型常作为昂贵真实过程的高效替代。但构建高质量代理模型通常需要大量数据采集。一个解决方案是将预训练的代理模型迁移到新任务，前提是任务间存在某种不变性。本文聚焦于从源函数到目标函数的非可微代理模型（如随机森林）迁移，假设其定义域之间存在未知仿射变换，且仅使用少量在目标上评估的数据点。此前研究针对可微模型（如高斯过程回归），通过调整仿射变换最小化转移数据上的经验损失。本文将该方法扩展至随机森林，并在广泛使用的黑箱优化基准测试集BBOB及四个真实世界迁移学习问题上进行了评估。结果表明，所提方法在减少数据需求和训练计算成本方面具有显著实际优势，尤其适用于复杂真实场景中的代理模型构建。
原文摘要 · Abstract (English)
Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes. However, constructing a high-quality surrogate model often demands extensive data acquisition. A solution to this issue is to transfer pre-trained surrogate models for new tasks, provided that certain invariances exist between tasks. This study focuses on transferring non-differentiable surrogate models (e.g., random forests) from a source function to a target function, where we assume their domains are related by an unknown affine transformation, using only a limited amount of transfer data points evaluated on the target. Previous research attempts to tackle this challenge for differentiable models, e.g., Gaussian process regression, which minimizes the empirical loss on the transfer data by tuning the affine transformations. In this paper, we extend the previous work to the random forest and assess its effectiveness on a widely-used artificial problem set - Black-Box Optimization Benchmark (BBOB) testbed, and on four real-world transfer learning problems. The results highlight the significant practical advantages of the proposed method, particularly in reducing both the data requirements and computational costs of training surrogate models for complex real-world scenarios.
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