III: Medium: Machine Learning with Humans in the Loop

机器学习越来越多地用于日常生活中常见的系统-从搜索引擎到在线教育再到智能家居。通过与用户的交互，这些系统了解世界以提高效率和性能。然而，构建有效和强大的人机交互学习（HIL）系统需要一个框架，同时将人类行为模型与机器学习算法的设计相集成，因为用户数据只是系统旨在获取的知识的间接映射，通过人类决策过程进行调解。 该项目采用跨学科的方法来开发有效的技术来设计人类交互式学习系统。该项目探讨了人类如何提供数据以及学习算法如何在包含三个方面的综合框架中使用这些数据。首先，该项目开发用户决策过程的模型，形式化可观察的用户行为如何映射到系统旨在获取的底层知识。其次，这些模型为连接用户和学习算法的界面设计提供信息，以适当地权衡所获取数据的数量和质量。第三，用户模型和界面激励新的机器学习设置和算法，以最大限度地提高学习效率。 通过为构建人类交互式学习系统的三个相互关联的组件开发一个集成框架-人类决策模型，信息获取接口和学习算法-该项目将影响未来广泛使用的系统设计，如非网络信息搜索，推荐和在线教育。

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.