*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Tess Malone, Communications Officer
Summary Sentence: Hard Thresholding–based Methods for Robust Learning
Full Summary:
Prateek JainTITLE: Hard Thresholding–based Methods for Robust Learning
ABSTRACT:
Learning in presence of outliers is a critical problem that can heavily affect performance of the learning algorithms in practice. In this talk, we present a general approach for learning with outliers, where we iteratively estimate the model parameters with estimated inliers and threshold out point which seems unlikely to be generated from the model to obtain more refined set of inliers. We instantiate this general approach for the outlier efficient PCA problem and demonstrate that it leads to nearly optimal solution in O(PCA) computation time.
BIO:
Prateek Jain is a member of the Machine Learning and Optimization and the Algorithms and Data Sciences Group at Microsoft Research, Bangalore, India. His research interests are in machine learning, non-convex optimization, high-dimensional statistics, and optimization algorithms in general. He also works on applications of machine learning to privacy, computer vision, text mining, and natural language processing.
