用马尔可夫决策过程先验，少测几次就找到最优解。
None To Optima in Few Shots: Bayesian Optimization with MDP Priors
- 用相关任务的优化轨迹构建MDP先验，学习高效搜索策略。
- 在新冠和癌症数据集上，用更少评估次数达到更好结果。
- 适合高成本实验场景，如药物研发、材料设计等关键领域。
贝叶斯优化（BO）是高效优化黑箱函数的工具，但其理论保证通常仅在渐近条件下成立。在药物发现或材料设计等真实世界应用中，每次评估代价高昂且耗时，导致多次评估不切实际。本文提出程序感知贝叶斯优化（ProfBO）算法，通过建模相关源任务的优化轨迹，利用马尔可夫决策过程（MDP）先验捕捉高效的优化过程知识。将这些先验嵌入可训练神经网络，并结合模型无关元学习实现对新目标任务的快速适应。在真实世界的新冠和癌症基准测试及超参数调优任务上的实验表明，ProfBO始终优于现有先进方法，在显著减少评估次数的同时获得高质量解，具备实际部署潜力。
原文摘要 · Abstract (English)
Bayesian Optimization (BO) is an efficient tool for optimizing black-box functions, but its theoretical guarantees typically hold in the asymptotic regime. In many critical real-world applications such as drug discovery or materials design, where each evaluation can be very costly and time-consuming, BO becomes impractical for many evaluations. In this paper, we introduce the Procedure-inFormed BO (ProfBO) algorithm, which solves black-box optimization with remarkably few function evaluations. At the heart of our algorithmic design are Markov Decision Process (MDP) priors that model optimization trajectories from related source tasks, thereby capturing procedural knowledge on efficient optimization. We embed these MDP priors into a prior-fitted neural network and employ model-agnostic meta-learning for fast adaptation to new target tasks. Experiments on real-world Covid and Cancer benchmarks and hyperparameter tuning tasks demonstrate that ProfBO consistently outperforms state-of-the-art methods by achieving high-quality solutions with significantly fewer evaluations, making it ready for practical deployment.
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