CAREER: New Paradigms for Online Machine Learning

Cornell University, Ithaca NY

Investigators

Abstract

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.