CAREER: Towards Better Understanding, Robustness, and Efficiency of Deep Learning

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Deep learning is a primary driving force behind many current intelligent decision-making systems and has achieved unprecedented success in various applications such as image processing, speech recognition, language translation, and game playing. However, the lack of adequate theoretical understanding is limiting the capacity to fully exploit the potential of deep learning in realistic environments, such as in security-sensitive or resource-constrained scenarios. This project aims to provide a thorough and systematic approach to understanding why practical deep-learning models succeed, under models of the data capturing the properties of real-world problems. This project will develop such an approach and employ the revealed principles in designing more robust and efficient deep-learning methods.The project will develop new theoretical models of properties of practical data leading to empirical success, and also provide frameworks for proving performance guarantees, including for learning in the presence of adversarial attacks or limited labeled data. It will also design new learning methods that are provably more robust and labeled-data efficient. This direction is still largely unexplored, despite significant recent research activities. The proposed theoretical and algorithmic solutions are possible through an interdisciplinary mix of tools from machine learning, statistics, and optimization. The proposed program is grounded in the investigator's prior work that includes both theoretical results and empirical validation. If successful, the proposed research can be transformational for modern intelligent systems by laying the foundations for further development. It will also help to solve new theoretical problems from practice that are not adequately addressed by current theory and will have lasting impacts on machine learning and optimization.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.