CRII: III: Integrating Domain Knowledge via Interactive Multi-Task Learning

CRII：III：通过交互式多任务学习整合领域知识

• 批准号: 1565596
• 负责人: Jiayu Zhou
• 金额: $ 17.49万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565596&HistoricalAwards=false
• 关键词: CRII; III; Integrating; Domain; Knowledge

The ever increasing availability of data has attracted a huge amount of effort building machine learning models from the data to unleash its hidden power. One ubiquitous finding about these machine learning tasks is that in most real-world applications the learning tasks are closely related to each other. Moreover, human experts in many domains can usually provide indispensable domain knowledge describing how these models are related. Maximally exploiting such knowledge is critical in building high quality machine learning models. This project will develop effective and efficient interactive algorithms and tools (including open source software) to enable knowledge discovery by integrating domain knowledge of task relatedness from human experts. The algorithms and tools developed in this project will directly impact biomedical informatics as they will be used to build disease progression models. The educational component of this project includes developing a new curriculum that incorporates research into the classroom and provides students from under-represented groups with opportunities to participate in research.Leveraging task relatedness, multi-task learning (MTL) simultaneously learns all related learning tasks and performs knowledge transfer among the tasks to improve the quality of models from all the tasks. Although there are numerous studies for MTL that assume different types of task relatedness, limited progress has been made in incorporating domain knowledge in MTL. This project will advance MTL by: (1) developing algorithms for knowledge aware multi-task feature learning which exploit domain knowledge of features to guide the selection of joint features from the learning tasks; (2) developing algorithms for knowledge aware multi-task relationship learning which utilize domain knowledge of tasks to guide the learning of task relationships; and (3) developing efficient and scalable optimization algorithms to facilitate effective interactive visualization. For further information see the project web page: http://jiayuzhou.github.io/projects/crii

数据可用性的不断增加吸引了大量的工作，从数据中构建机器学习模型，以释放其隐藏的力量。关于这些机器学习任务的一个普遍发现是，在大多数现实世界的应用中，学习任务彼此密切相关。此外，许多领域的人类专家通常可以提供描述这些模型如何相关的不可或缺的领域知识。最大限度地利用这些知识对于构建高质量的机器学习模型至关重要。该项目将开发有效和高效的交互式算法和工具（包括开源软件），通过整合人类专家的任务相关性领域知识来实现知识发现。该项目中开发的算法和工具将直接影响生物医学信息学，因为它们将用于构建疾病进展模型。该项目的教育部分包括开发一个新的课程，将研究纳入课堂，并为来自代表性不足群体的学生提供参与研究的机会。利用任务相关性，多任务学习（MTL）同时学习所有相关的学习任务，并在任务之间进行知识转移，以提高所有任务的模型质量。虽然有许多MTL的研究，假设不同类型的任务相关性，有限的进展已经取得了在MTL领域知识。本研究将通过以下几个方面来推进MTL：（1）开发知识感知的多任务特征学习算法，利用特征的领域知识来指导从学习任务中选择联合特征;（2）开发知识感知的多任务关系学习算法，利用任务的领域知识来指导任务关系的学习;以及（3）开发高效和可扩展的优化算法以促进有效的交互式可视化。欲了解更多信息，请访问项目网页：http://jiayuzhou.github.io/projects/crii