Structured classification and regression

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。拟议的项目旨在为高维结构化数据开发新的统计理论和方法。该项目的灵感来自于两个重要的生物学应用中出现的具有挑战性的问题：疾病基因识别和基因功能发现，其中一个中心问题是如何有效地利用问题结构来处理发现过程中的高度统计不确定性。该项目由两个主要部分组成：子网络分析和结构化学习。在子网络分析方面，PI和他的合作者将开发新的技术来提取特定的低维子网络结构，其中网络由有向图或无向图描述。在结构化学习方面，PI和他的合作者将开发用于部分多标签层次分类的新的大间隔技术，特别是致力于在层次约束和各种层次损失函数下的准确预测。其目标是在目前最好的技术基础上实现预测准确性的实质性改进。此外，还将开发针对实际问题的计算工具，并提供最佳或接近最佳的解决方案。拟议的项目将从根本上解决结构化数据分析中的重要问题。它将激发人们对研究新出现的问题的研究兴趣，并将促进统计学家与计算机科学和生物医学等其他领域的科学家之间的合作。该研究计划将在几个研究领域产生影响，特别是在文件管理和探索、自动机器处理、生物医学研究和社会科学方面。该教育计划将教学与研究相结合，让学生接触到最先进的研究，并为培训和学习创造一个跨学科的学习环境。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The proposed project aims to develop new statistical theory and methodology for high-dimensional structured data. The project is inspired by challenging problems that arise in two important biological applications: disease gene identification and gene function discovery, where one central issue is how to utilize problem structure effectively to deal with high statistical uncertainty in a discovery process. The project consists of two major components: subnetwork analysis and structured learning. With regard to subnetwork analysis, the PI and his collaborators will develop new techniques for extracting a certain low-dimensional subnetwork structure, where a network is described by a directed or an undirected graph. With regard to structured learning, the PI and his collaborators will develop new large margin techniques for partial multi-label hierarchical classification, with particular effort focused on accurate prediction under hierarchical constraints and various hierarchical loss functions. The goal is to achieve a substantial improvement on predictive accuracy over the current best techniques. In addition, computational tools will be developed to target real problems and to provide optimal or near-optimal solutions. The proposed project will address fundamentally important issues in structured data analysis. It will generate research interest for studying emerging problems, and will promote collaborations between statisticians and scientists from other fields such as computer science and biomedical science. The research program will have an impact in several areas of research, particularly in document management and exploration, automatic machine processing, biomedical research, and social science. The educational program will integrate teaching with research to get students exposed to state-of-the-art research, and to create an interdisciplinary learning environment for training and learning.