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Computer Science @ Rochester
Monday, March 25, 2013
10:45 AM
CSB 209
Raman Arora
Toyata Technological Institute of Chicago
Stochastic approximation algorithms for large-scale unsupervised learning
The nature of signal processing and machine learning has evolved dramatically over the years as we try to investigate increasingly intricate, dynamic and large-scale systems. This development is accompanied by an explosion of massive, unlabeled, multimodal, corrupted and very high-dimensional "big data", which poses new challenges for efficient analysis and learning. In this talk, I will advocate a learning approach based on "stochastic approximation", wherein a single data point is processed at each iteration using a computationally simple update, to address these challenges. I will start by presenting a stochastic approximation (SA) meta-algorithm for unsupervised learning with large high-dimensional datasets. I will then describe the application of the SA algorithm to a multiview learning framework, where multiple modalities are available at the time of training but not for prediction at test time, and a similarity-based learning framework where data is observed only in the form of pairwise similarities. I will conclude with a theoretical analysis of the SA algorithm and a discussion about the pitfalls of SA approaches and the remedies thereof.

Bio: Raman Arora received his B.E. degree from NSIT, Delhi, India, in 2001, and M.S. and Ph.D. degrees from the University of Wisconsin-Madison in 2005 and 2009, respectively. He worked as a Research Associate at University of Washington, Seattle, from 2009 to 2011 and was a visiting researcher at Microsoft Research (MSR) during the summer of 2011. He is currently a Postdoctoral Researcher at the Toyota Technological Institute at Chicago. His research interests include large-scale machine learning, online learning, speech recognition and statistical signal processing.