CAREER: New Paradigms for Online Machine Learning

我们生活在一个技术驱动的世界，用户每天都与大规模的自动化系统进行交互。在线推荐系统、搜索引擎和个性化医疗只是以机器学习（ML）算法为核心的系统的几个例子。ML作为一个领域的长期成功依赖于将其转化为一种易于使用的无缝技术，并具有严格的可证明的性能保证。此外，为了对社会产生积极的影响，ML技术需要具备应对任何大型多用户系统所带来的社会挑战的能力。这个CAREER项目的首要目标是使社会责任ML成为一种易于访问的黑盒技术，适用于大型多用户交互系统。 具体而言，该项目侧重于三个具体挑战。第一个挑战是通过自动化设计任务特定ML算法的过程，使ML成为即插即用技术。第二个挑战是为现代应用开发机器学习方法，例如预测社交网络中的用户偏好，其中数据不断发展并复杂地相互关联。第三个挑战是为推荐系统开发理论和算法，这些推荐系统对社会负责，不会损害用户的利益。近年来，探索概率论和序列预测问题之间的内在联系，导致了在线学习的统一理论和算法设计原则。这个职业项目将建立在这些发展的基础上。利用概率论中的Burkholder方法和数学规划领域的进展，该项目旨在自动设计新的有效的在线学习算法。基于最近开发的在线放松的想法，该项目将引入新的方法来设计计算效率高的算法，用于从互连的数据点学习。最后，使用和扩展经典统计学的思想来处理控制和讨厌的变量，该项目将开发新的推荐系统方法，可以避免极化用户。CAREER计划将通过开发与ML相关的新教育组件来推进STEM教育。“大众机器学习”研讨会将与康奈尔大学的“计算机领域的女性”共同组织，旨在让女性和代表性不足的少数群体参与进来，并让本科生在成长期接触机器学习领域的研究和就业机会。该奖项反映了NSF的法定使命，并被认为值得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来支持。

We live in a technology-driven world, where users interact with large-scale, automated systems on a daily basis. Online recommendation systems, search engines and personalized medicine are just a few examples of systems that use Machine Learning (ML) algorithms at their core. The long term success of ML as a field relies on transforming it into an easily usable, seamless technology with rigorous, provable guarantees on performance. Further, to have a positive impact on society, ML technologies need to be equipped to handle the social challenges that accompany any large multi-user systems. The overarching goal of this CAREER project is to make socially-responsible ML a readily accessible black-box technology that is applicable in large multi-user interactive systems. In particular, the project focuses on three concrete challenges. The first challenge is to make ML a plug-and-play technology by automating the process of designing task specific ML algorithms. The second challenge is to develop ML methods for modern applications such as predicting user preferences in social networks, where data is evolving and complexly interconnected. The third challenge is to develop theory and algorithms for recommendation systems that are socially responsible and do not polarize its users. In recent years, exploring inherent connections between probability theory and sequential prediction problems have lead to a unifying theory and algorithm design principles for online learning. This CAREER project will build on these developments. Using the so called Burkholder method from probability theory and advances in the field of mathematical programming, the project will aim at automating the process of designing new and effective online learning algorithms. Building on the recently developed idea of online relaxations, the project will introduce novel methodology for designing computationally efficient algorithms for learning from interconnected data points. Finally, using and extending ideas from classical statistics to deal with control and nuisance variables, the project will develop new methods for recommender systems that can avoid polarizing users. The CAREER program will advance STEM education by developing new educational components related to ML. ``Machine Learning for the Masses'' workshops will be co-organized with Women In Computing at Cornell aimed at involving women and underrepresented minorities and exposing undergraduates to research and job opportunities in the field of ML during their formative years.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.