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

Principal Investigator: Jiayu Zhou [email:]

Students Supported: Kaixiang Lin, Qi Wang

The ever increasing availability of data has attracted a huge amount of machine learning models being built 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 is developing 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 the 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 research.

1. Multi-Task Feature Interaction Learning. Kaixiang Lin, Jianpeng Xu, Inci M. Baytas, Shuiwang Ji and Jiayu Zhou. KDD 2016. [Paper]

Software: MALSAR (Multi-tAsk Learning via StructurAl Regularization) package includes a set of multi-task learning formulations and efficient algorithms.

Read Jiayu's homepage for more about his research.