AF: Small: Foundations for Collaborative and Information-Limited Machine Learning

机器学习越来越多地被用于整个社会，并在广泛的应用中。 企业使用机器学习系统进行决策支持，互联网网站使用机器学习来更好地与用户互动，我们的个人设备使用机器学习来适应我们的需求，我们的汽车开始使用数据训练系统来提高安全性。 这些应用带来了新的机会，也带来了新的问题。机会包括系统通过协作更快地学习和适应的潜力，以及算法决策的隐私和公平性。 该项目旨在发展对这些机会和问题的新的基本理解，以帮助指导更有效，更适应和更公平的机器学习方法的开发。 该项目还将支持关于这些问题的教育研讨会，并更广泛地支持这些主题的年轻科学家的教育和培训。具体而言，该项目有以下四个主要目标：（1）协作机器学习。具有相关学习任务的设备如何最好地协作，以便仅从少量数据中有效学习，以及如何解决隐私和相关问题？ (2)性能测试和误差外推。 这个推力的目的是开发方法，从少量的标记数据，可以可靠地估计如何以及给定的学习算法或表示类将执行，如果给定一个更大的标记数据样本。 (3)半监督学习半监督学习是指将标记数据和未标记数据联合收割机结合起来的方法，即使在标记数据有限的情况下也能很好地学习。这项工作的目的是开发一种方法的理论基础，该方法基于在未标记的数据中显式地学习数据，然后使用这些数据来指导如何在标记的数据上进行学习。(4)学习中的公平。最近，人们对算法决策（例如是否向申请人提供贷款）表示了极大的担忧，这些决策可能会不公平地歧视某些类别的人。这项工作旨在为解决这类问题开发更好的理论理解、工具和保证。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning increasingly is being used throughout society, and in a wide range of applications. Businesses use machine learning systems for decision support, internet sites use machine learning to better interact with users, our personal devices use machine learning to adapt to our needs, and our cars are beginning to use data-trained systems to improve safety. These applications bring up new opportunities as well as new concerns. Opportunities include the potential for systems to more rapidly learn and adapt through collaboration, and concerns include privacy and the fairness of algorithmically-made decisions. This project is aimed at developing new foundational understanding of these opportunities and concerns, to help guide the development of more efficient, more adaptive, and fairer, machine learning methods. This project additionally will support educational workshops on these issues, and more broadly will support the education and training of young scientists on these topics.Specifically, this project has the following four main thrusts: (1) Collaborative Machine Learning. How can devices with related learning tasks best collaborate to learn efficiently from only a modest amount of data, and how can privacy and related concerns be addressed? (2) Property Testing and Error Extrapolation. This thrust aims to develop methods that, from a small amount of labeled data, can reliably estimate how well a given learning algorithm or representation class would perform if given a much larger labeled data sample. (3) Semi-Supervised Learning. Semi-supervised learning refers to methods that combine labeled and unlabeled data, to learn well even when labeled data is limited. This work aims to develop theoretical foundations for an approach based on explicitly learning regularities within the unlabeled data and then using these to guide how learning is performed over the labeled data. (4) Fairness in Learning. There has recently been substantial concern about algorithmic decisions (such as whether to offer an applicant a loan) that could unfairly discriminate against certain classes of people. This work aims to develop improved theoretical understanding, tools, and guarantees for tackling these kinds of problems.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.