AI 中文总结
针对标准多保真贝叶斯优化在现实场景中因最高保真度函数成本过高导致的次优问题，提出结合历史高保真数据与任务描述符的方法，经合成函数及化学、超参数优化问题验证有效。
AI 中文摘要
黑盒优化是科学与工程领域普遍存在的问题，常涉及昂贵的目标函数，同时存在成本更低的低保真替代函数。多保真贝叶斯优化（MF-BO）是解决该问题的合理方法，在查询目标函数时利用不同保真度间的相关性。然而，对于许多重要的多保真贝叶斯优化任务，真正的最高保真度函数成本过高，无法纳入优化循环。不过，从业者通常拥有来自先前实验的黄金标准数据（最高保真度函数的观测值），这些数据可能为当前任务提供信息。例如，在分子优化中，化学家常使用各种计算机模拟筛选前k个候选分子，之后公布它们的真实目标函数值。本研究证明，即使在理想假设下，标准多保真贝叶斯优化算法在上述现实场景中存在次优性。接着，我们通过结合历史高保真数据及任务描述符（可明确给出或从非结构化元数据中提取）来缓解该问题。我们在合成函数以及化学、超参数优化领域的现实问题上验证了所提方法的有效性。
英文摘要
Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available. Multi-fidelity Bayesian optimization (MF-BO) is a principled approach to this problem, leveraging correlations across different fidelities when querying the objective. However, for many important MF-BO tasks, the true highest-fidelity function is prohibitively expensive to be part of the optimization loop. Nevertheless, practitioners often have gold standard data (observations of the highest-fidelity function) obtained from previous experiments that might provide information for the current task. For instance, in molecular optimization, chemists often pick the top-$k$ candidate molecules using various computer simulations, and later reveal their true objective function values. In this work, we demonstrate the suboptimality of standard MF-BO algorithms in the real-world scenarios above, even under ideal assumptions. Next, we mitigate this problem by incorporating historical high-fidelity data accompanied by task descriptors---which can be explicitly given or extracted from unstructured metadata. We demonstrate the effectiveness of our methods on synthetic functions, as well as real-world problems in chemistry and hyperparameter optimization.
