Machine learning is increasingly used in systems common in daily life -- from search engines to online education to smart homes. Through interactions with their users, these systems learn about the world to improve efficiency and performance. Building effective and robust Human-Interactive Learning (HIL) systems, however, requires a framework that simultaneously integrates models of human behavior with the design of machine learning algorithms, because user data is only an indirect mapping, mediated through the human decision-making process, of the knowledge the system aims to elicit. This project takes an interdisciplinary approach to developing effective techniques to design human-interactive learning systems.
This project explores how humans provide data and how learning algorithms use this data in an integrated framework that encompasses three aspects. First, the project develops models of the user decision process, formalizing how observable user actions map to the underlying knowledge the system aims to acquire. Second, these models inform the design of the interface that connects the user and the learning algorithm to suitably trade off the quantity and quality of the data acquired. Third, the user model and interface motivate new machine learning settings and algorithms to maximize learning efficiency. By developing an integrated framework for the three interconnected components for building Human Interactive Learning Systems -- human decision models, information-elicitation interfaces, and learning algorithms -- this project will impact future designs of widely-used systems such as non-web information search, recommendation, and online education.
