CAREER: Learning from Heterogeneous Populations in Small Data Regime with Applications to Preference and Metric Learning

职业：在小数据体制中向异质群体学习并应用于偏好和度量学习

• 批准号: 2238876
• 负责人: Ramya Vinayak
• 金额: $ 58.71万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238876&HistoricalAwards=false
• 关键词: CAREER; Learning; Heterogeneous; Populations; Small

Understanding how humans represent different concepts, perceive different options, and make judgements based on them plays a vital role in cognitive and behavioral sciences, consumer recommendation systems, individualized education, crowdsourced democracy and in quantifying survey data for social sciences and policy making. Preference and metric learning using judgment from humans have emerged as powerful tools to learn such representations. Most of these learning algorithms, however, are limited to studying models that are averaged over the population and do not capture the variations among the diverse set of people comprising the population. This project aims to close this gap by developing novel models, analyzing their fundamental limits, and designing algorithms with guarantees that can be learned at different scales of granularity. The results of this project have the potential to usher in a new paradigm in preference and metric learning. This project will also have significant educational and outreach impact through course modules for graduate and undergraduate students, research mentoring for undergraduate students, and public outreach programs.From biological sciences to social sciences, many scientific studies involve societal-scale datasets collected over heterogeneous populations, e.g., different ages, demographics, etc. Such datasets also usually have only a few observations per individual (small data regime). In general, off-the-shelf machine learning algorithms are not built with consideration to the statistical challenges arising from issues like heterogeneity and small data. This project addresses the challenges that arise when learning from heterogeneous populations in small data regimes in preference and metric learning by developing novel models, theoretical foundations in terms of fundamental limits, and practical algorithms with guarantees for learning from heterogeneous data at different levels of granularity. Specifically, the project aims to establish fundamental limits, develop models and algorithms with theoretical guarantees for (1) learning distribution of preferences over a population, (2) learning metrics at subgroup levels, and (3) learning individual variability by leveraging common structures and priors learned over the population.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解人类如何代表不同的概念，感知不同的选择并基于他们做出判断，在认知和行为科学，消费者建议系统，个性化教育，人拥挤的民主以及量化社会科学和政策制定的调查数据中起着至关重要的作用。使用人类判断的偏好和度量学习已成为学习这种表示的强大工具。但是，这些学习算法中的大多数仅限于研究平均人口的模型，并且不会捕获包括人口组成的各种各样的人群之间的差异。该项目的目的是通过开发新型模型，分析其基本限制以及设计算法，以确保可以在不同的粒度规模上学习，旨在缩小这一差距。该项目的结果有可能引入偏好和度量学习方面的新范式。该项目还将通过研究生和本科生的课程模块，本科生的研究指导以及公共宣传计划。从生物科学到社会科学，许多科学研究都涉及社会规模的数据集，这些数据集涉及在异构种群中，例如不同的数据，但只有少数数据涉及（例如，众多数据），许多科学的数据涉及，许多科学研究涉及社会科学。一般而言，现成的机器学习算法并非考虑到异质性和小数据等问题引起的统计挑战。该项目解决了通过开发新型模型，基于基本限制的理论基础和实用算法的偏好和度量学习中的非均质人群学习时出现的挑战，并保证从不同级别的颗粒状水平的异构数据中学习。具体而言，该项目旨在建立基本限制，开发模型和算法，并具有理论保证，可（1）学习偏好的学习分布，（（2）亚组级别的学习指标以及（3）通过利用公共结构和普里人来学习个人的差异，以实现NSF的构建范围，并依靠NSS的知名度，并反映了NSF的规定，并反映了NSF的规定，并反映了范围的范围。审查标准。