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CAREER: Active Learning through Rich and Transparent Interactions

职业：通过丰富和透明的互动主动学习

基本信息

批准号：
1350337
负责人：
Mustafa Bilgic
金额：
$ 54.99万
依托单位：
Illinois Institute of Technology
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-01 至 2020-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350337&HistoricalAwards=false
关键词：
CAREER Active Learning through Rich

项目摘要

Machine learning models are trained on data that are annotated (labeled) by humans. The accuracy of the trained models generally improves with the number of annotated data examples. Yet, annotating takes time, money, and effort. Active learning aims to minimize the costs by determining which exemples are most informative and directing the human labeler to them. Improvements in active learning will lower the costs associated with data annotation and lead to faster implementations of intelligent systems for a range of applications including robotics, speech technology, error and anomaly detection (for example in medicine, financial fraud, and condition-based maintenance of infrastructure), targeted advertising, human-computer interfaces, and bioinformatics.In traditional active learning approaches, algorithms are limited in the types of information they can acquire, and they often do not provide any rationale to the user as to why a particular exemplar is chosen for annotation. This CAREER project develops a new paradigm dubbed "rich and transparent active learning." This new paradigm opens a communication channel between algorithms and users whereby they can exchange a rich set of queries, answers, and explanations. By using rich feedback from users the algorithms will be able to learn the target concept more economically, reducing the resources required to build an accurate predictive model. By explaining their reasoning, these algorithms will achieve transparency, build trust, and open themselves to scrutiny. Towards that end, the project develops methods that allow algorithms to use a rich set of queries for resource-efficient model training, and generate explanations that are informative but not overwhelming for the users. The methods developed build on expected loss minimization, information theory, and principles from human-computer interaction. Approaches are evaluated using publicly available datasets and user studies carried out as part of the project. The project develops case studies on two high-impact real-world problems: detecting fraudulent health-care claims, and identifying patients at risk of disease.The rich and transparent active learning paradigm provides unique educational opportunities. In contrast to standard machine learning algorithms, operated as black boxes, interactive and transparent machine learning is expected to raise students' interest and motivation for data science. Two PhD and several undergraduate and high school students are being trained under this award. A new graduate course on interactive machine learning is being developed. Finally the PI ensures effective outreach to under-represented groups by partnering with a Chicago public high school whose student population includes 90% minorities.

机器学习模型是在人类注释（标记）的数据上进行训练的。训练模型的准确性通常随着注释数据示例的数量而提高。然而，注释需要时间、金钱和精力。主动学习的目的是通过确定哪些例子是最具信息量的，并指导人类标注者来最小化成本。主动学习的改进将降低与数据注释相关的成本，并导致智能系统更快地实现一系列应用，包括机器人、语音技术、错误和异常检测（例如医学、金融欺诈和基础设施的状态维护）、目标广告、人机界面和生物信息学。在传统的主动学习方法中，算法可以获取的信息类型有限，而且它们通常不向用户提供任何理由，说明为什么选择一个特定的范例进行注释。这个CAREER项目开发了一种被称为“丰富和透明的主动学习”的新范式。这个新范例在算法和用户之间打开了一个通信通道，通过这个通道，他们可以交换一组丰富的查询、答案和解释。通过使用来自用户的丰富反馈，算法将能够更经济地学习目标概念，减少构建准确预测模型所需的资源。通过解释它们的推理，这些算法将实现透明度，建立信任，并开放自己接受审查。为此，该项目开发了一些方法，允许算法使用一组丰富的查询来进行资源高效的模型训练，并生成信息丰富但不会让用户不知所措的解释。开发的方法建立在预期损失最小化、信息论和人机交互原理的基础上。使用公开可用的数据集和作为项目一部分进行的用户研究来评估方法。该项目就两个影响很大的现实问题开展个案研究：发现欺诈性保健索赔和确定有患病风险的病人。丰富而透明的主动学习模式提供了独特的教育机会。与黑箱操作的标准机器学习算法相比，交互式和透明的机器学习有望提高学生对数据科学的兴趣和动力。目前正在培养2名博士和多名本科生、高中生。一门关于交互式机器学习的新研究生课程正在开发中。最后，PI通过与芝加哥一所公立高中合作，确保有效地接触到代表性不足的群体，该高中的学生中有90%是少数族裔。