Bayesian Optimization with Ensemble Learning Models and Adaptive Expected Improvement
Bayesian Optimization with Ensemble Learning Models and Adaptive Expected Improvement
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DOI：
10.1109/icassp49357.2023.10095008
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Konstantinos D. Polyzos;Qin Lu;G. Giannakis
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文献类型：
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作者：
Konstantinos D. Polyzos;Qin Lu;G. Giannakis
优化一个评估成本高昂的黑盒函数出现在机器学习和人工智能应用的范围内，包括药物发现、机器人技术中的策略优化以及学习模型的超参数调整等。贝叶斯优化（BO）提供了一个原则性的框架，使用有限数量的函数评估来找到这些函数的全局最优值。BO依赖于统计代理模型来主动选择新的查询点，这通常由高斯过程（GP）捕获。不同于大多数现有的方法，铰链上的一个单一的GP代理模型与预先选定的核函数，可能会限制所寻求的功能，特别是在有限的评估预算的表现力，本工作提出了一个加权的集成的GP作为代理模型。基于提倡的高斯混合（GM）后验，EGP框架在数据实时到达时适应最适合的代理模型，提供更丰富的函数空间。对于下一个评估点的获取，基于EGP的后验与自适应期望改进（EI）标准相结合，以平衡搜索空间的探索和利用。一组基准合成功能和两个机器人任务的数值测试，证明了所提出的方法令人印象深刻的好处。
Optimizing a black-box function that is expensive to evaluate emerges in a gamut of machine learning and artificial intelligence applications including drug discovery, policy optimization in robotics, and hyperparameter tuning of learning models to list a few. Bayesian optimization (BO) provides a principled framework to find the global optimum of such functions using a limited number of function evaluations. BO relies on a statistical surrogate model to actively select new query points, that is typically captured by a Gaussian process (GP). Unlike most existing approaches that hinge on a single GP surrogate model with a pre-selected kernel function that may confine the expressiveness of the sought function especially under the limited evaluation budget, the present work puts forth a weighted ensemble of GPs as a surrogate model. Building on the advocated Gaussian mixture (GM) posterior, the EGP framework adapts to the most fitted surrogate model as data arrive on-the-fly, offering a richer function space. For the acquisition of next evaluation points, the EGP-based posterior is coupled with an adaptive expected improvement (EI) criterion to balance exploration and exploitation of the search space. Numerical tests on a set of benchmark synthetic functions and two robotic tasks, demonstrate the impressive benefits of the proposed approach.