CAREER: Exact Algorithms for Learning Latent Structure

职业：学习潜在结构的精确算法

• 批准号: 1745125
• 负责人: David Sontag
• 金额: $ 35.08万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1745125&HistoricalAwards=false
• 关键词: CAREER; Exact; Algorithms; Learning; Latent

One of the fundamental tasks in science is to infer the causal relationships between variables from data, and to discover hidden phenomena that may affect their outcome. We can attempt to automate this scientific process by searching over probabilistic models of how the observed data might be influenced by unobserved (latent) factors or variables. Machine learning of such models provides insight into the underlying domain and a means of predicting the latent factors. However, it is challenging to search over the exponentially many models, and existing algorithms are unable to scale to large amounts of data.The goal of this CAREER award will provide novel algorithms to circumvent this computational intractability. Based on a classical idea in statistics called the method-of-moments, the new algorithms will be applied in bioinformatics to discover regulatory modules from disease expression profiles, and in health care to predict a patient's clinical state using data from their electronic medical record. A key component of the project is to involve high school students from disadvantaged backgrounds in the research to inspire them to pursue STEM careers.The project advances machine learning by introducing several new techniques for unsupervised and semi-supervised learning of Bayesian networks. The project overcomes the computational challenges associated with maximum-likelihood estimation by developing new method-of-moment based algorithms for learning latent variable models, focusing on settings where inference itself may be intractable. This includes Bayesian networks of discrete variables where a top layer consists of latent factors and a bottom layer consists of the observed data, a form of discrete factor analysis. The proposed algorithms run in polynomial time and are guaranteed to learn a close approximation to the true model.The techniques developed as part of this project have the potential to be transformative in the social and natural sciences by enabling the efficient and accurate discovery of latent variables from discrete data. Furthermore, in collaboration with emergency department clinicians, the new algorithms will be applied to learn models relating diseases to symptoms from noisy and incomplete data that is routinely collected as part of electronic medical records. This will advance the field of machine learning in health care by providing algorithms that generalize between institutions without the need for a large amount of labeled training data.The insights about exploratory data analysis developed as part of this project will be integrated into innovative curriculum in data science, both as part of an undergraduate class and new Master's classes. The project will bring students from nearby high schools to NYU throughout the academic year and during the summer to learn about machine learning through participation in the proposed research, having them use the unsupervised learning algorithms to discover new medical insights. The PI will also develop and deliver tutorials on machine learning to clinicians and the health care industry.

科学的基本任务之一是从数据中推断变量之间的因果关系，并发现可能影响其结果的隐藏现象。我们可以尝试通过搜索观察到的数据如何受到未观察到的（潜在的）因素或变量影响的概率模型来自动化这一科学过程。对这些模型的机器学习提供了对潜在领域的深入了解和预测潜在因素的方法。然而，它是具有挑战性的搜索指数许多模型，和现有的算法无法扩展到大量的数据。这个CAREER奖的目标将提供新的算法，以规避这种计算上的困难。基于统计学中的经典思想，即矩量法，新算法将应用于生物信息学，从疾病表达谱中发现调控模块，并在医疗保健中使用电子病历中的数据预测患者的临床状态。该项目的一个关键组成部分是让来自弱势背景的高中生参与研究，激励他们追求STEM职业。该项目通过引入几种用于贝叶斯网络无监督和半监督学习的新技术来推进机器学习。该项目克服了与最大似然估计相关的计算挑战，通过开发新的基于矩量法的算法来学习潜变量模型，重点关注推理本身可能难以处理的设置。这包括离散变量的贝叶斯网络，其中顶层由潜在因素组成，底层由观察到的数据组成，这是离散因素分析的一种形式。所提出的算法在多项式时间内运行，并保证学习接近真实模型。作为该项目的一部分开发的技术有可能在社会和自然科学中进行变革，使从离散数据中高效准确地发现潜在变量。此外，在与急诊科临床医生的合作中，新算法将被应用于从作为电子病历的一部分定期收集的嘈杂和不完整数据中学习疾病与症状相关的模型。这将通过提供无需大量标记训练数据即可在机构之间推广的算法，推动医疗保健领域的机器学习。作为该项目的一部分，开发的探索性数据分析见解将被整合到数据科学的创新课程中，作为本科课程和新硕士课程的一部分。该项目将在整个学年和夏季将附近高中的学生带到纽约大学，通过参与拟议的研究来学习机器学习，让他们使用无监督学习算法来发现新的医学见解。PI还将为临床医生和医疗保健行业开发和提供机器学习教程。