用低成本仿真加速高精度模型训练，提升效率与精度。
Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
- 基于伯努利参数互信息，实现多保真度批量主动学习
- 在固定计算预算下，预测准确率显著优于基线方法
- 适合依赖昂贵仿真的科学工程领域研究者
许多科学与工程问题依赖于昂贵的计算模拟，多保真度方法可加速参数空间探索。本文研究在二分类输出场景下，利用高斯过程（GP）模型高效分配模拟预算。提出伯努利参数互信息（BPMI）算法，通过链函数的一阶泰勒展开规避概率空间中互信息计算的不可解性。在两个合成测试案例及一个复杂真实应用——激光点火火箭燃烧室模拟中评估BPMI，结果表明其在所有实验中均表现更优，在固定计算预算下获得更高预测精度。
原文摘要 · Abstract (English)
Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study efficient allocation of a simulation budget using a Gaussian Process (GP) model in the binary simulation output case. This paper introduces Bernoulli Parameter Mutual Information (BPMI), a batch active learning algorithm for multi-fidelity GP classifiers. BPMI circumvents the intractability of calculating mutual information in the probability space by employing a first-order Taylor expansion of the link function. We evaluate BPMI against several baselines on two synthetic test cases and a complex, real-world application involving the simulation of a laser-ignited rocket combustor. In all experiments, BPMI demonstrates superior performance, achieving higher predictive accuracy for a fixed computational budget.
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