标题： 基于贝叶斯自适应直接搜索的模型拟合实用贝叶斯优化 显示英文标题

标题： Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search

摘要： 计算神经科学等领域中的计算模型通常通过随机模拟或数值近似进行评估。 拟合这些模型意味着在一个复杂的、可能带有噪声的参数空间上解决一个困难的优化问题。 贝叶斯优化（BO）已在工程和机器学习领域成功应用于解决昂贵的黑箱问题。 在这里，我们探讨是否可以将BO作为模型拟合的一种通用工具。 首先，我们提出了一种新颖的混合BO算法，即贝叶斯自适应直接搜索（BADS），该算法以可承受的计算开销实现了与典型模型运行时间相当的竞争性能。 然后，我们在一组来自行为学、认知科学和计算神经科学六项研究的真实数据和模型的模型拟合问题上，对BADS与许多常见的和最先进的非凸、无导数优化器进行了广泛的基准测试。 在默认设置下，BADS始终能找到与其他方法（包括“纯”BO）相当或更好的解决方案，展示了高级BO技术（特别是BADS）作为通用模型拟合工具的巨大潜力。 显示英文摘要

摘要： Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explore whether BO can be applied as a general tool for model fitting. First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models. We then perform an extensive benchmark of BADS vs. many common and state-of-the-art nonconvex, derivative-free optimizers, on a set of model-fitting problems with real data and models from six studies in behavioral, cognitive, and computational neuroscience. With default settings, BADS consistently finds comparable or better solutions than other methods, including `vanilla' BO, showing great promise for advanced BO techniques, and BADS in particular, as a general model-fitting tool.