ABSTRACT

Mathematical optimization lies at the heart of machine learning (ML) and artificial intelligence (AI) algorithms. Key challenges herein are to decide what criteria to optimize, and what algorithms to use for performing the optimization. These challenges underlie the motivation for the present project. More specifically, this project seeks to make progress on three fundamental topics in optimization for ML: (i) theoretical foundations for a rich new class of optimization problems that can be solved efficiently (i.e., in a computationally tractable manner); (ii) a set of algorithms that apply to large-scale optimization problems in machine learning (e.g., for accelerating the training of neural networks); and (iii) theory that seeks to understand and explain why do neural networks succeed in practice. By focusing on topics of foundational importance, this project should spur a variety of followup research that deepends the connection of ML and AI with both mathematics and the applied sciences. More broadly, the this project may have a lasting societal impact too, primarily because of (i) its focus on optimization particularly relevant to ML and AI; (2) the non-traditional application domains it connects with (e.g., synthetic biology); and (3) because the investigator is in an environment that fosters such impact (namely, the Institute for Data, Systems, and Society (IDSS), a cross-disciplinary institute at MIT whose mission to drive solutions to problems of societal relevance). Finally, the project has an education centric focus; it involves intellectual and professional development of students, as well as development of curricular material based on the topics of research covered herein.

This project lays out an ambitious agenda to develop foundational theory for geometric optimization, large-scale nonconvex optimization, and deep neural networks. The research on geometric optimization (which is a powerful new subclass of nonconvex optimization), is originally motivated by applications in ML and statistics; however, it stands to have a broader impact across all disciplines that consume optimization. The investigator seeks to develop a theory of polynomial time optimization for a class strictly larger than usual convex optimization problems, and thereby endow practitioners with new polynomial time tools and models; if successful, this investigation could open an entire subarea of research and applications. Beyond geometric optimization, the project also focuses on large-scale nonconvex optimization and on the theory of optimization and generalization for deep learning. Within these topics, the project will address key theoretical challenges, develop scalable new algorithms that could greatly speed up neural network training, and also make progress that reduces the gap between the theory and real-world practice of nonconvex 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.

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