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lossy_compression_for_bigdata-_first_steps

Abstract: Two key challenges in fitting BigData problems into a lossy compression framework are (i) the selection of an appropriate distortion measure, and (ii) characterizing the performance of distributed systems. Inspired by real systems, like web search, which return a list of likely data entries indexed by likelihood, we study the “logarithmic loss” distortion function in a multiterminal setting, thus addressing both challenges. In particular, we characterize the rate-distortion region for two (generally open) multiterminal source coding problems when distortion is measured under logarithmic loss. In addition to the main results, I'll discuss applications to machine learning, estimation, and combinatorics.

Bio: Thomas Courtade received the B.S. degree in Electrical Engineering from Michigan Technological University in 2007, and the M.S. and Ph.D. degrees in Electrical Engineering from UCLA in 2008 and 2012, respectively. In 2012, he was awarded a Postdoctoral Research Fellowship at the Center for Science of Information. He currently holds this position, and currently resides at Stanford University. His recent honors include a Distinguished Ph.D. Dissertation award from the UCLA Department of Electrical Engineering and a Best Student Paper Award at the 2012 International Symposium on Information Theory.

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