CAREER: Active Machine Learning for Automating Scientific Discovery

收集和编目数据的特征通常比分析数据以确定感兴趣的属性要容易得多。这种情况在自然科学和工程领域普遍存在，在这些领域，深入调查可能需要人工干预、昂贵的计算机模拟或昂贵的实验室实验。人类正处于数据革命的临界点，我们收集和存储信息的能力可能会超过我们从数据中提取有用知识的能力。主动机器学习为这一困境提供了一个解决方案：我们自适应地设计昂贵的实验，由底层过程的统计模型指导，以最有效地利用有限的资源。许多研究已经证明，主动机器学习是一种很有前途的自动化科学发现工具；然而，现代程序目前很难为从业者采用。有效地使用可用的工具需要相当多的专业知识，特别是在机器学习领域持续快速发展的情况下。该项目将把主动机器学习的应用转变为科学和工程问题，开发新的实验程序，开创科学发现的新范式。该项目还将通过自动化大大增加这些方法对非专业人员的可用性，促进机器学习进一步整合到跨学科的实践中。所有的研究都将由问题驱动，并通过科学和工程领域的应用数据进行验证，包括材料科学、药物发现、天文学和机器人技术。该项目的研究目标将伴随一项全面的教育计划，旨在向广泛的未来科学家和工程师介绍主动机器学习。该项目将涉及两个广泛的调查主题，对应于主动学习管道的两个关键组成部分：(1)实验政策和(2)建模。主动学习过程的核心是它的策略，它决定分析哪些数据。在构建主动学习系统时，一个主要的挑战是为给定的学习目标开发一个计算效率高、经验有效的策略。这不是一个直截了当的任务：最优的程序在计算上是不可行的，自然的近似可能会受到短视和贪婪行为的影响。该项目将提高自动科学发现政策的性能和理论理解，开发和研究主动科学发现的现有和新的范例。贯穿本次调查的主题是非近视决策，其中一个原因是每个决策对整个学习任务的影响。算法的发展将伴随着广泛的理论研究，建立基本的学习界限，并在可能的情况下寻求有效的近似方案。调查的第二个重点将是从数据中对复杂过程进行建模，因为政策的成功取决于信息模型的指导。由于固有的训练数据有限，主动学习的模型选择变得困难，并且考虑模型的不确定性通常是至关重要的。该项目将研究与主动学习相结合的自动模型选择，推进自动化机器学习的新兴领域，以创建不需要专家设计或调整的鲁棒、全自动主动学习系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

It is often much easier to collect and catalog features of data than to analyze data to determine properties of interest. Such settings are pervasive in the natural sciences and engineering, where in-depth investigation can require human intervention, expensive computer simulation, or costly laboratory experiments. Humanity is at the tipping point of a data revolution, and our ability to collect and store information will likely outpace our capacity to extract useful knowledge from data. Active machine learning provides a solution to this dilemma: we adaptively design expensive experiments guided by statistical models of the underlying process to make the most-effective use of limited resources. Numerous studies have established active machine learning as a promising tool for automating scientific discovery; however, modern procedures are currently difficult for practitioners to adopt. Considerable expertise is required to effectively use the available tools, especially as the field of machine learning continues to develop rapidly. This project will transform the application of active machine learning to problems from science and engineering, developing novel experimental procedures and pioneering new paradigms of scientific discovery. This project will also dramatically increase the availability of these methods to non-experts through automation, facilitating the further integration of machine learning into practice across disciplines. All research will be motivated by problems and validated on data from applications across science and engineering, including materials science, drug discovery, astronomy, and robotics. The project's research objectives will be accompanied by a comprehensive education plan designed to introduce active machine learning to a broad range of future scientists and engineers.The project will entail two broad themes of inquiry, corresponding to the two critical components of an active learning pipeline: (1) experimental policies and (2) modeling. (1): The core of an active learning procedure is its policy, which decides which data to analyze. A primary challenge when building an active learning system is developing a computationally efficient and empirically effective policy for the given learning objective. This is not a straightforward task: the optimal procedure is computationally infeasible and natural approximations can suffer from myopic, greedy behavior. This project will improve the performance and theoretical understanding of policies for automated scientific discovery, developing and studying both established and novel paradigms for active scientific discovery. A theme throughout this investigation will be nonmyopic decision making, where one reasons about the impact of each decision on the entire learning task. Algorithmic development will be accompanied by extensive theoretical study, establishing fundamental learning bounds and seeking efficient approximation schemes when possible. (2): The second thrust of the investigation will be on modeling complex processes from data, as a policy's success hinges on being guided by an informative model. Model selection for active learning is rendered difficult by inherently limited training data, and accounting for model uncertainty is often critical. The project will investigate automated model selection inline with active learning, advancing the nascent field of automated machine learning to create robust, fully automated active learning systems that do not require expert design or tuning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.