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来支持决策，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，关注的问题包括隐私和算法决策的公平性。该项目旨在发展对这些机会和问题的新的基础理解，以帮助指导开发更有效、更适应性和更公平的机器学习方法。该项目还将支持有关这些问题的教育讲习班，并在更广泛的范围内支持有关这些主题的年轻科学家的教育和培训。具体来说，该项目有以下四个主要重点：(1)协同机器学习。具有相关学习任务的设备如何最好地协作，从少量数据中高效地学习？如何解决隐私和相关问题？(2)性能测试和误差外推。这一主旨旨在开发方法，从少量标记数据中，可以可靠地估计给定的学习算法或表示类在给定更大的标记数据样本时的表现。(3)半监督学习。半监督学习是指将标记数据和未标记数据结合起来，即使在标记数据有限的情况下也能很好地学习的方法。这项工作旨在为一种基于在未标记数据中明确学习规律的方法开发理论基础，然后使用这些方法来指导如何对标记数据进行学习。(4)学习公平。最近，人们对算法决策（例如是否向申请人提供贷款）可能不公平地歧视某些阶层的人感到非常担忧。这项工作旨在为解决这类问题提供改进的理论认识、工具和保证。该奖项反映了美国国家科学基金会的法定使命，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.