CAREER: Robust, scalable, reliable machine learning

机器学习越来越多地应用于大规模、任务关键的问题，目的是做出影响到广大个人的就业、储蓄、健康和安全的决策。机器学习可能极大地影响和改变人们的生活，这就要求机器学习方法必须是健壮的、可解释的和可理解的-而不是黑箱。这项研究开发了新的技术，这些技术既是计算上的动机，也是理论上可靠的，可以在规模上进行稳健的机器学习。这项工作是在三个现代应用类别的背景下进行的。(1)经济学家感兴趣的是分析小额信贷的效果，小额信贷是向贫困地区的个人提供的小额贷款，目的是消除贫困。(2)生物学家对使用单细胞RNA测序数据来了解细胞之间的关系和发育轨迹很感兴趣。(3)物联网(IoT)将在建筑物内、交通基础设施内、道路上的车辆和许多其他传感器来源的能源读数中生成丰富的复杂数据。PI直接与领域专家合作，以便在应用程序领域产生直接、广泛的影响。在该项目的一个教育部分，PI是在麻省理工学院开发统计、数据科学和统计机器学习的新研究生课程和学位的核心部分。本提案中的方法和应用包含在一门关于现代机器学习方法的新课程中。作为已建立的女性技术计划(WTP)的一部分，PI还在开发一份高中水平的机器学习入门课程。健壮性和可解释性问题尤其出现在具有非平凡的空间和时间依赖性的领域，这些领域的数据量往往是巨大的，而且从业者在接触特定数据集之前通常对该领域有一些专业知识。这些正是现有机器学习方法发展不够完善的领域。需要将结构知识应用于问题，建议使用贝叶斯方法，这种方法可以通过先验和建模假设结合这些知识。然而，为了兑现这些方法的承诺，实际方法需要对假设以及噪声或对抗性数据具有健壮性，以免这些数据以从业者不理解的方式改变重要决策。这项研究结合了统计物理学的进展，以评估数据分析对假设和数据值的敏感性。为了实现拟议的健壮和可理解的机器学习框架的优势，实践者必须面对极端的可扩展性问题-无论是从计算角度还是从建模角度。在计算方面，这项研究建立在从计算几何到尺度再到现代大小数据集的最新进展之上。在建模方面，请注意，虽然小规模的问题表现出密集的时空依赖关系，但大型问题往往比较稀疏，实用的方法必须反映这种稀疏性才能在规模上可靠。这项工作结合了概率论的进步来对稀疏物联网网络进行建模。这项提议是高度跨学科的-汇集了机器学习、统计学、物理学、理论计算机科学、概率理论和系统的想法，并将这些想法应用于小额信贷、单细胞RNA测序、传感器网络、国际贸易和工业应用，包括大规模的客户服务。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning is increasingly deployed in large-scale, mission critical problems for the purpose of making decisions that affect a vast number of individuals' employment, savings, health, and safety. The potential for machine learning to dramatically impact and change people's lives necessitates that machine learning methods be robust, explainable, and understandable---rather than black-box. This research develops new techniques that are both computationally motivated and theoretically sound for robust machine learning at scale. The work is situated in the context of three modern classes of applications. (1) Economists are interested in analyzing the efficacy of microcredit, small loans to individuals in impoverished areas with the goal of eliminating poverty. (2) Biologists are interested in using single-cell RNA sequencing data to understand cells' relationships and development trajectories. (3) The Internet of Things (IoT) is poised to generate a wealth of complex data across energy readings in buildings, within transportation infrastructure, from vehicles on the road, and from many other sensor sources. The PI is working directly with area experts so as to have immediate, broad impact across application domains. In an educational component of the project, the PI is a core part of developing a new graduate curriculum and degree in statistics, data science, and statistical machine learning at MIT. The methods and applications in this proposal feature in a new course on modern machine learning methods. The PI is also developing a high-school level introduction to machine learning as part of the established Women's Technology Program (WTP).The issues of robustness and explainability particularly arise in domains with nontrivial spatial and temporal dependencies, where the amount of data is often massive, and where practitioners typically have some expert knowledge about the domain before engaging with a particular dataset. These are precisely the domains where existing machine learning methodologies are less well-developed. The need to bring structural knowledge to bear on the problem suggests the use of Bayesian methods, which can incorporate this knowledge via prior and modeling assumptions. To live up to the promise of these methods, though, practical approaches need to be robust to assumptions as well as to noisy or adversarial data, lest this data change important decisions in ways not understood by the practitioner. This research incorporates advances in statistical physics to assess the sensitivity of a data analysis to assumptions and data values. And to realize the advantages of the proposed robust and understandable machine learning framework, practitioners must face extreme scalability issues---both from a computational perspective as well as a modeling perspective. On the computational side, this research builds on recent advances from computational geometry to scale to data sets at modern sizes. On the modeling side, note that while small-scale problems exhibit dense spatio-temporal dependencies, large-scale problems tend to be sparser, and practical approaches must reflect this sparsity to be reliable at scale. This work incorporates advances in probability theory to model sparse IoT networks. This proposal is highly interdisciplinary---bringing together ideas from machine learning, statistics, physics, theoretical computer science, probability theory, and systems and applying these ideas to microcredit, single-cell RNA sequencing, sensor networks, international trade, and industrial applications including customer service at scale.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.