[Ml-stat-talks] Po-Ling Loh, Today at 4:30pm, Sherrerd Hall 101
rigollet at Princeton.EDU
Mon Feb 10 09:44:18 EST 2014
Po-Ling work on non-convex optimization for statistical problems is very interesting.
Everyone in this list should be interested.
=== ORFE Colloquium Announcement ===
DATE: Today, February 10, 2014
LOCATION: Sherrerd Hall room 101
SPEAKER: Po-Ling Loh, Department of Statistics , University of California, Berkeley
TITLE: Nonconvex Methods for High-dimensional Regression with Noisy and Missing Data
ABSTRACT: Noisy and missing data are prevalent in many real-world statistical estimation problems. Popular techniques for handling nonidealities in data, such as imputation and expectation-maximization, are often difficult to analyze theoretically and/or terminate in local optima of nonconvex functions -- these problems are only exacerbated in high-dimensional settings. We present new methods for obtaining high-dimensional regression estimators in the presence of corrupted data, and provide theoretical guarantees for the statistical consistency of our methods. Although our estimators also arise as minima of nonconvex functions, we show the rather surprising result that all stationary points are clustered around a global minimum. Motivated by a fundamental connection between linear regression and inverse covariance matrices, we demonstrate an important application of our method for graphical model estimation with noisy and missing data.
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