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03846aam a2200349Ii 4500 001 AE77F7CC533D11EC9185B7CB4CECA4DB 003 SILO 005 20211202010028 008 210616s2021 enka b 001 0 eng d 020 $a 1108616798 020 $a 9781108616799 020 $a 1108427138 020 $a 9781108427135 035 $a (OCoLC)1256665515 040 $a OKX $b eng $e rda $c OKX $d OKX $d OCLCO $d EQO $d SILO 050 04 $a QA76 I54 2021 245 00 $a Information-theoretic methods in data science / $c edited by Miguel R. D. Rodrigues, Yonina C. Eldar. 264 1 $a Cambridge, United Kingdom : $b Cambridge University Press, $c 2021. 300 $a xxi, 538 pages : $b illustrations (black and white) ; $c 25 cm 504 $a Includes bibliographical references and index. 505 0 $a Introduction to information theory and data science / Miguel R. D. Rodrigues, Stark C. Draper, Waheed U. Bajwa, and Yonina C. Eldar -- An information-theoretic approach to analog-to-digital compression / Alon Kipnis, Yonica C. Eldar, and Andrea J. Goldsmith -- Compressed sensing via compression codes / Shirin Jalali and H. Vincent Poor -- Information-theoretic bounds on sketching / Mert Pilanci -- Sample complexity bounds for dictionary learning from vector- and tensor-valued data / Zahra Shakeri, Anand D. Sarwate, and Waheed U. Bajwa -- Uncertainty relations and sparse signal recovery / Erwin Riegler and Helmut BoÌlcskei -- Understanding phase transitions via mutual information and MMSE / Galen Reeves and Henry D. Pfister -- Computing choice: learning distributions over permutations / Devavrat Shah -- Universal clustering / Ravi Kiran Raman and Lav R. Varshney -- Information-theoretic stability and generalization / Maxim Raginsky, Alexander Rakhlin, and Aolin Xu -- Information bottleneck and representation learning / Pablo Piantanida and Leonardo Rey Vega -- Fundamental limits in model selection for modern data analysis / Jie Ding, Yuhong Yang, and Vahid Torakh -- Statistical problems with planted structures: information-theoretical and computational limits / Yihong Wu and Jiaming Xu -- Distributed statistical inference with compressed data / Wenwen Zhao and Lifeng Lai -- Network functional compression / Soheil Feizi and Muriel MeÌdard -- An introductory guide to Fano's inequality with applications in statistical estimation / Jonathan Scarlett and Volkan Cevher. 520 $a "Learn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics."--Publisher's description. 650 0 $a Data mining. 650 0 $a Information theory. 650 0 $a Machine learning. 700 1 $a Rodrigues, Miguel R. D., $e editor. 700 1 $a Eldar, Yonina C., $e editor. 776 08 $i ebook version : $z 9781108606530 941 $a 1 952 $l USUX851 $d 20220303011130.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=AE77F7CC533D11EC9185B7CB4CECA4DB 994 $a C0 $b IWAInitiate Another SILO Locator Search