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

不断增长的数据可用性吸引了大量的努力，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，在大多数真实世界的应用程序中，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不可或缺的领域知识，描述这些模型是如何相关的。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算法和工具(包括开放源码软件)，通过整合人类专家关于任务相关性的领域知识来实现知识发现。在这个项目中开发的算法和工具将直接影响生物医学信息学，因为它们将被用于建立疾病进展模型。该项目的教育部分包括开发一种新的课程，将研究纳入课堂，并为代表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。尽管有许多关于MTL的研究假设了不同类型的任务相关性，但在将领域知识纳入MTL方面取得的进展有限。该项目将通过以下几个方面推进MTL：(1)开发利用特征领域知识来指导学习任务中联合特征选择的知识感知多任务特征学习算法；(2)开发利用任务领域知识来指导任务关系学习的知识感知多任务关系学习算法；以及(3)开发高效且可扩展的优化算法，以促进有效的交互可视化。有关更多信息，请参阅项目网页：http://jiayuzhou.github.io/projects/crii

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