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Tuesday, February 5, 2013 at 4:15pm
Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic combination of m-estimators. Each of the estimators resolves the computation-accuracy tradeoff differently, and taken together they span a continuous spectrum of computation-accuracy tradeoff resolutions. We prove the consistency of the estimators, provide formulas for their asymptotic variance, statistical robustness, and computational complexity. We discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators when the computational resources are insufficient. They also demonstrate that in some cases reduced computational complexity is associated with robustness thereby increasing statistical accuracy.
Bio: Guy Lebanon is an associate professor of computing at the Georgia Institute of Technology. His main research areas are statistical machine learning, computational statistics, and information visualization. He also serves as the associate director of the FODAVA project and is a visiting scientist at Google Research. Before coming to Georgia Tech, Dr. Lebanon was an assistant professor of statistics and electrical and computer engineering at Purdue University. He received his PhD in 2005 from Carnegie Mellon University and BA, and MS degrees from Technion - Israel Institute of Technology. Dr. Lebanon has authored over 50 refereed publications. He was the program co-chair of the 2012 ACM CIKM Conference, and guest editor of Data Mining and Knowledge Discovery journal. He received the NSF CAREER Award, the Yahoo Faculty Research and Engagement Award, and is a Siebel Scholar.