论文标题
具有定量序列因素的实验建模和主动学习
Modeling and Active Learning for Experiments with Quantitative-Sequence Factors
论文作者
论文摘要
一种旨在确定一系列因素的最佳量的新型实验正在引起医学，生物工程和许多其他学科的大量关注。这样的研究需要同时优化数量和几个组件的序列顺序，这些组件称为定量序列（QS）因子。鉴于在此类实验中的较大和半差异的解决方案空间，使用少量的实验试验可以有效地识别最佳或近乎最佳的解决方案是一项非平凡的任务。为了应对这一挑战，我们提出了一种新型的主动学习方法，称为QS学习，以实现有效的建模和有效优化QS因子的实验。 QS学习由三个部分组成：一种基于新型映射的加斯高斯流程（MAGP）模型，有效的全局优化方案（QS-EGO）和一类新的最佳设计（QS-Design）。研究了所提出方法的理论特性，并开发了使用分析梯度的优化技术。通过对淋巴瘤治疗和几项模拟研究的真实药物实验证明了该方法的性能。
A new type of experiment that aims to determine the optimal quantities of a sequence of factors is eliciting considerable attention in medical science, bioengineering, and many other disciplines. Such studies require the simultaneous optimization of both quantities and the sequence orders of several components which are called quantitative-sequence (QS) factors. Given the large and semi-discrete solution spaces in such experiments, efficiently identifying optimal or near-optimal solutions by using a small number of experimental trials is a nontrivial task. To address this challenge, we propose a novel active learning approach, called QS-learning, to enable effective modeling and efficient optimization for experiments with QS factors. QS-learning consists of three parts: a novel mapping-based additive Gaussian process (MaGP) model, an efficient global optimization scheme (QS-EGO), and a new class of optimal designs (QS-design). The theoretical properties of the proposed method are investigated, and optimization techniques using analytical gradients are developed. The performance of the proposed method is demonstrated via a real drug experiment on lymphoma treatment and several simulation studies.
