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a_new_approach_to_robustness_and_flexibility_in_high-dimensions
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Title: A New Approach to Robustness and Flexibility in High-dimensions Abstract: High-dimensional problems are those where the number of variables to estimate/decide on vastly outnumber the number of observations. Several popular methods for the same impose structural models on the data (e.g. low-rank, sparse, Markov assumptions etc.). These methods however are both very fragile to gross/adversarial corruptions, and overly restrictive in their modeling capability. We propose the simultaneous use of more than structural model for high-dimensional problems. Our approach yields several new methods that are much more widely applicable, and significantly more robust, than existing ones - often with only slightly larger computational complexity. We present new methods, and corresponding analytical results, for (a) PCA in the presence of arbitrary outliers and corruptions (b) Robust Collaborative filtering (c) Multiple sparse regression/compressed sensing with partially shared sparsity (d) Graph clustering Our methods are based on convex optimization. Bio: Sujay Sanghavi is an Assistant Professor in ECE at UT Austin. He obtained his PhD from UIUC in 2006, and was a postdoctoral associate in LIDS, MIT until 2008. Sujay's research lies at the intersection of large-scale networks (communication and social), and statistical machine learning and inference. He got the NSF CAREER award in 2010.

a_new_approach_to_robustness_and_flexibility_in_high-dimensions.txt · Last modified: 2016/09/01 19:15 (external edit)