双向信息流提升贝叶斯优化采样效率，加速收敛。
Bidirectional Information Flow (BIF) -- A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization
- 构建父子模型双向反馈机制，实现在线学习中的信息互换。
- 在真实神经刺激任务中，父模型$R^2$提升4倍，子模型提升3倍。
- 适合需要模块化复用与高效优化的复杂系统设计场景。
分层高斯过程（H-GP）将问题分解为多个子任务，使不同模型分别处理各部分，适用于具有内在层级结构的问题。然而，传统H-GP仅支持信息单向传递，限制了采样效率和收敛速度。本文提出双向信息流（BIF）框架，在在线训练中建立父模型与子模型之间的双向信息交换。BIF保持分层模型的模块化结构——父模型整合子任务知识，同时引入自顶向下的反馈持续优化子模型。这种双向互动显著提升采样效率，增强训练鲁棒性，并支持已学子任务模型的模块化复用。在合成数据和真实神经刺激优化任务上，BIF相比传统H-GP方法，父模型$R^2$最高提升4倍，子模型提升3倍。
原文摘要 · Abstract (English)
Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing for different models to address each part, making them well-suited for problems with inherent hierarchical structure. However, typical H-GP models do not fully take advantage of this structure, only sending information up or down the hierarchy. This one-way coupling limits sample efficiency and slows convergence. We propose Bidirectional Information Flow (BIF), an efficient H-GP framework that establishes bidirectional information exchange between parent and child models in H-GPs for online training. BIF retains the modular structure of hierarchical models - the parent combines subtask knowledge from children GPs - while introducing top-down feedback to continually refine children models during online learning. This mutual exchange improves sample efficiency, enables robust training, and allows modular reuse of learned subtask models. BIF outperforms conventional H-GP Bayesian Optimization methods, achieving up to 4x and 3x higher $R^2$ scores for the parent and children respectively, on synthetic and real-world neurostimulation optimization tasks.
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