改进高维贝叶斯优化的局部模型稳定性，提升搜索效率。
Rethinking Trust Region Bayesian Optimization in High Dimensions
- 自适应调整核函数尺度，随维度和信任区域大小动态变化
- 在合成与真实轨迹规划任务中显著优于标准TuRBO
- 适合高维黑箱优化场景，尤其对复杂目标函数有效
信任域贝叶斯优化（TuRBO）是一种缓解高维黑箱优化中维数灾难的有效策略。然而，不恰当的长度尺度设计会导致信任域内局部高斯过程（GP）模型退化，从而在高维下表现不佳。本文表明，随着问题维度 $D$ 和信任域边长 $L$ 的变化，TuRBO 的局部 GP 可能过于复杂或过于简单。为此，我们提出一种简单变体 AdaScale-TuRBO，其将 GP 长度尺度同时按问题维度和信任域大小进行缩放，从而保持核几何结构并维持一致的先验复杂度。实验结果表明，AdaScale-TuRBO 在合成基准和真实世界轨迹规划任务中均能稳健超越标准 TuRBO 及其他主流高维贝叶斯优化方法。
原文摘要 · Abstract (English)
Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimization. However, inappropriate lengthscale design can cause the local Gaussian process (GP) model within the trust region to degenerate, leading to suboptimal performance in high dimensions. In this work, we show that TuRBO's local GP may remain either excessively complex or overly simple as the dimension $D$ and trust region side length $L$ vary. To address this issue, we propose a straightforward variant, AdaScale-TuRBO, which scales the GP lengthscale with both the problem dimension and trust region size, thereby preserving kernel geometry and maintaining consistent prior complexity. Empirically, we show that AdaScale-TuRBO can robustly outperform standard TuRBO and other popular high-dimensional BO methods on synthetic benchmarks and real-world trajectory planning tasks.
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