CAREER: Towards a theory of machine learning with strategic interactions

该奖项的全部或部分资金来自《2021年美国救援计划法案》(Public Law 117-2)。机器学习(ML)算法使用观察到的样本数据来发现一般模式，然后用这些模式进行预测。与人类数据和利益相关者交互的学习系统(如用于个性化医疗、内容管理、金融市场、招聘和贷款的系统)发生在复杂的社会和经济环境中。在这种广泛的应用中，学习算法和人之间存在反馈回路，影响学习过程的质量和人的幸福感。这些反馈循环目前还没有被ML的经典理论捕捉到，以一种特别的、非数学的方式处理它们可能会产生重大的社会反响。该项目将为解决学习系统和人之间的互动问题开发一个严格的数学框架，并将借鉴广泛的学术传统和领域，包括计算理论、人工智能、经济学和计算。该项目还涉及教育和社区建设计划，使下一代学生能够通过跨学科研究为满足新兴和现代需求的机器学习理论做出贡献。该项目将为确保学习算法在日常社会和经济力量存在的情况下的性能以及从使用机器学习系统产生的社会和经济力量的完整性奠定理论基础。为了实现这一点，研究人员将考虑对抗性、战略性和协作性互动。对于对抗性和长期的战略互动，该项目将探索在线决策过程，并提供在线学习算法，在有更现实的适应性和非短视的战略代理人的情况下表现良好。该项目还探讨了战略性游戏和交流对学习和现有信息质量的长期社会影响，着眼于理解和解决偏见和两极分化的信念。此外，为了充分获得协作交互的好处，该项目将使学习算法的性能与参与的代理的需求和偏好保持一致。这将导致协作学习协议的设计，这些协议具有不同的私密性、统计效率和公平性。该项目还包括外展、指导和教育计划，以补充其技术目标，包括将不同社区的成员聚集在一起并帮助为该领域制定议程的“在战略行为中学习”的研讨会系列，以及“学习理论联盟”，这是一个支持机器学习理论界的大型指导倡议。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Machine learning (ML) algorithms use observed sampled data to uncover general patterns that can then be used for making predictions. Learning systems that interact with human data and stakeholders (such as those used in personalized medicine, content curation, financial markets, hiring, and lending) take place in a complex social and economic context. In this wide range of applications, there are feedback loops between learning algorithms and people that impact the quality of the learning process and the wellbeing of people. These feedback loops are currently not captured by the classical theory of ML, and handling them in an ad hoc, non-mathematical, way could have major social repercussions. This project will develop a rigorous mathematical framework for addressing interactions between learning systems and people and will draw from a wide range of academic traditions and fields, including Theory of Computing, Artificial Intelligence, Economics and Computation. This project also addresses educational and community building plans for enabling the next generations of students to contribute to a theory of machine learning for emerging and modern needs through cross-disciplinary research.This project will build a theoretical foundation for ensuring both the performance of learning algorithms in the presence of everyday social and economic forces and the integrity of social and economic forces that are born out of the use of machine-learning systems. To achieve this, the investigator will consider adversarial, strategic, and collaborative interactions. For adversarial and long-term strategic interactions, the project will explore online decision processes and contribute online learning algorithms that perform well in presence of more realistic adaptive and non-myopic strategic agents. The project also explores the long-term social impact of strategic play and communication on learning and quality of available information, with an eye towards understanding and addressing biased and polarized beliefs. Additionally, to reap the full benefit of collaborative interactions, the project will align the performance of learning algorithms with the needs and preferences of participating agents. This will lead to the design of collaborative learning protocols that are differentially private, statistically efficient, and equitable. This project also includes outreach, mentoring, and educational plans that will complement its technical goals, including a workshop series on "Learning in presence of Strategic Behavior" that brings together members of different communities and helps set an agenda for the field and "Learning Theory Alliance" that is a large-scale mentoring initiative for supporting the machine learning theory community.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.