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249 Mon, 7/30/2012, 2:00 PM - 3:50 PM CC-Room 29D
Selection and Classification for High-Dimensional Data — Contributed Papers
IMS , Section on Statistical Learning and Data Mining
Chair(s): Alexander Aue, University of California at Davis
2:05 PM Classification Based on a Permanental Process with Cyclic Approximation Jie Yang, University of Illinois at Chicago ; Klaus Miescke, University of Illinois at Chicago ; Peter McCullagh, The University of Chicago
2:20 PM Estimating Sparse Precision Matrices from Data with Missing Values Mladen Kolar, Carnegie Mellon University ; Eric P. Xing, Carnegie Mellon University
2:35 PM Sparse Mean Estimation in High-Dimensional Data Shota Katayama, Osaka University
2:50 PM Robust High-Dimensional Regression: Sparse Least Trimmed Squares Andreas Alfons, K. U. Leuven ; Christophe Croux, K. U. Leuven ; Sarah Gelper, Erasmus University Rotterdam
3:05 PM Optimal Streaming Feature Selection Robert Stine, University of Pennsylvania
3:20 PM A Covariance Matrix Estimator Based on the Singular Wishart Likelihood Samprit Banerjee, Weill Cornell Medical College ; Stefano Monni, Weill Cornell Medical College
3:35 PM In Practice, Many Models Fit the Data Equally Well John Boscardin, University of California at San Francisco



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