CAREER: Learning Algorithms with Robustness and Efficiency Guarantees

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

From achieving super-human performance in image classification to beating human champions in complex games, machine learning has seen enormous success in recent years. To unravel the full potential of machine learning and extend its reach, computer scientists seek to develop a comprehensive theory that explains the power and limitations of existing methods and offers avenues for improvement. This project aims to contribute in this direction by tackling two pressing challenges that limit the broader applicability of machine learning: (i) solving more complex tasks with multiple actions and decisions over time and (ii) learning from corrupted data. The goal is to study the performance and potential shortcomings of existing approaches through a unified framework and develop novel algorithms that are provably robust and efficient. In tandem with its research goals, the project incorporates the development of undergraduate and graduate courses at UW Madison, the training of graduate students, and research opportunities for undergraduates. In more detail, the project focuses on the themes of learning under noisy data and learning combinatorial algorithms from data. In the first theme, the investigator will build upon recent advances in dealing with noisy labels extending the results to more complex settings like multi-class classification, and will design more efficient methods that perform well in many practical situations. In the second theme, the investigator will study the use of Machine Learning to automatically develop efficient algorithms tailored to a specific application by bridging the areas of data-driven algorithm design and reinforcement learning. The focus in both themes will be on the statistical and computational complexity of the proposed methods. The project has the potential to offer a new paradigm of algorithm design through learning and to robustify machine learning systems enabling new application domains that involve large amounts of noise. 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.