Information-theoretic methods in data science /
Rodrigues, Miguel R. D.(Miguel Raul Dias),editor.
Eldar, Yonina C.,editor.
"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.
Includes bibliographical references and index.
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 Bölcskei -- 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 Médard -- An introductory guide to Fano's inequality with applications in statistical estimation / Jonathan Scarlett and  Volkan Cevher.
"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.
Data mining.
Information theory.
Machine learning.