CAREER: Statistical Inference for Bayesian Machine Learning

职业：贝叶斯机器学习的统计推断

• 批准号: 1944740
• 负责人: Veronika Rockova
• 金额: $ 40万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944740&HistoricalAwards=false
• 关键词: CAREER; Statistical; Inference; Bayesian; Machine

With visible successes on a broad range of predictive problems, the role of machine learning (ML) has become increasingly recognized across a wide array of application domains ranging from economics to electronic commerce. In medicine, for instance, machine learning is routinely deployed for image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis from fMR images, and text analysis of radiology reports using natural language processing. In spite of this nascent ML trend, there has been significant reluctance to delegate decision making entirely to machine intelligence. This has been largely due to the absence of a formal statistical framework for uncertainty quantification and interpretability. This yawning gap between theory and practice presents new exciting research opportunities for theoretical developments that will justify and unleash the potential machine-assisted decision making in real life. This project has two broad objectives. The first one is motivated by the currently unmet demand for theoretical justification of widely used Bayesian machine learning tools. The second objective is developing practicable methodology for interpretable machine learning, which is essential for gleaning insights into the behavior of real-world processes. The research outlined in this project will bridge current conceptual divides between statistics and machine learning by solidifying Bayesian machine-assisted inference as statistically valid so that it can be safely used to tackle complex scientific problems arising in data-rich environments including imaging, personalized medicine, business analytics, marketing and economics. There has been a growing realization of the potential of Bayesian machine learning as a platform that can provide both flexible modeling, accurate predictions as well as coherent uncertainty statements. In particular, Bayesian Additive Regression Trees (BART) has emerged as one of today's most effective machine learning methods under minimal assumptions. BART has already proved itself to be broadly effective at unveiling structure hidden in high dimensional data across a wide variety of contemporary applications. Its theoretical properties for statistical inference, however, have remained unknown. The detailed research agenda of the first three goals aims at obtaining an in-depth theoretical understanding of BART (as well as some aspects of Bayesian deep learning) through the investigation of (1) uncertainty quantification and confidence set constructions, (2) adaptability to spatially inhomogeneous objects, (3) asymptotic normality for causal inference. The completion of these objectives will significantly advance the current frontier of semi-parametric and non-parametric Bayesian theory. The principal investigator will develop new scalable tools for interpretable machine learning which will extend the reach of ML to many new application areas and problem types involving big data.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.

随着在一系列预测问题上的显著成功，机器学习(ML)的作用在从经济学到电子商务的广泛应用领域得到了越来越多的认可。例如，在医学领域，机器学习通常用于图像分割、配准、计算机辅助检测和诊断、从FMR图像进行大脑功能或活动分析，以及使用自然语言处理对放射学报告进行文本分析。尽管出现了这种新兴的ML趋势，但人们一直非常不愿意将决策完全委托给机器智能。这在很大程度上是因为缺乏一个用于不确定性、量化和可解释性的正式统计框架。理论和实践之间的这种巨大差距为理论发展提供了新的令人兴奋的研究机会，这些发展将证明并释放现实生活中潜在的机器辅助决策。这个项目有两个广泛的目标。第一个原因是目前广泛使用的贝叶斯机器学习工具在理论上的合理性需求尚未得到满足。第二个目标是开发可解释的机器学习的实用方法，这对于收集对真实世界过程的行为的洞察是必不可少的。该项目概述的研究将通过巩固贝叶斯机器辅助推理在统计学上的有效性，弥合目前统计学和机器学习之间的概念鸿沟，以便它可以安全地用于解决在数据丰富的环境中产生的复杂科学问题，包括成像、个性化医学、商业分析、营销和经济学。人们越来越意识到贝叶斯机器学习作为一种平台的潜力，它可以提供灵活的建模、准确的预测以及连贯的不确定性陈述。特别是，贝叶斯加性回归树(BART)已经成为当今最有效的机器学习方法之一，在最小的假设下。BART已经证明了自己在揭示高维数据中隐藏在各种当代应用程序中的结构方面是广泛有效的。然而，它用于统计推断的理论性质仍然未知。前三个目标的详细研究议程旨在通过研究(1)不确定性量化和置信度集构造，(2)对空间不均匀对象的适应性，(3)因果推理的渐近正态，获得对BART(以及贝叶斯深度学习的某些方面)的深入理论理解。这些目标的完成将极大地推进当前半参数和非参数贝叶斯理论的前沿。首席研究员将为可解释的机器学习开发新的可扩展工具，这将把ML的覆盖范围扩展到许多涉及大数据的新应用领域和问题类型。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。