Past Event: Babuška Forum

Stochastic Optimization Methods for Large-Scale Machine Learning

Raghu Bollapragada, Assistant Professor, Operations Research and Industrial Engineering, UT Austin.

Abstract

Nonlinear optimization problems arise in a wide range of applications, from acoustic/geophysical inversion to deep learning. The scale, computational cost, and difficulty of these models make classical optimization techniques impractical. To address these challenges, we have developed new stochastic optimization methods that, in addition, are well-suited for distributed computing implementations. Our techniques employ adaptive sampling strategies that judiciously use the available data to achieve efficiency and scalability. We provide global convergence and complexity results for strongly convex and non-convex functions and illustrate our algorithm's performance on machine learning models. 

 

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Biography

Raghu Bollapragada is an assistant professor in the Operations Research and Industrial Engineering graduate program at the University of Texas at Austin (UT). Before joining UT, he was a postdoctoral researcher in the Mathematics and Computer Science Division at Argonne National Laboratory. He received both PhD and MS degrees in Industrial Engineering and Management Sciences from Northwestern University. During his graduate study, he was a visiting researcher at INRIA, Paris. His current research interests are in nonlinear optimization and its applications in machine learning.  He has received the IEMS Nemhauser Dissertation Award for best dissertation, the IEMS Arthur P. Hurter Award for outstanding academic excellence, the McCormick terminal year fellowship for outstanding terminal-year PhD candidate, and the Walter P. Murphy Fellowship at Northwestern University.