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Information-Theoretic Methods in Data Science

Information-Theoretic Methods in Data Science

c.£79.99

Miguel Rodrigues, Stark Draper, Waheed Bajwa, Yonina Eldar, Alon Knipis, Andrea Goldsmith, Shirin Jalali, Vincent Poor, Mert Pillanci, Zahra Shakeri, Anand Sarwate, Erwin Riegler, Helmut Bölcskei, Galen Reeves, Henry Pfister, Devavrat Shah, Ravi Raman, Lav Varshney, Maxim Raginsky, Alexander Rakhlin, Aolin Xu, Pablo Piantanida, Leonardo Rey Vega, Jie Ding, Yuhong Yang, Vahid Tarokh, Yihong Wu, Jiaming Xu, Wenwen Zhao, Lifeng Lai, Soheil Feizi, Muriel Médard, Jonathan Scarlett, Volkan Cevher
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  • Publication planned for: January 2020
  • availability: Not yet published - available from January 2020
  • format: Hardback
  • isbn: 9781108427135

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About the Authors
  • 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.

    • The first book covering the interface between information theory and data science
    • Provides a tutorial approach to the subject, with each chapter including a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list
    • Uses consistent notation and definitions throughout
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    Product details

    • Publication planned for: January 2020
    • format: Hardback
    • isbn: 9781108427135
    • dimensions: 244 x 170 mm
    • availability: Not yet published - available from January 2020
  • Table of Contents

    1. Introduction Miguel Rodrigues, Stark Draper, Waheed Bajwa and Yonina Eldar
    2. An information theoretic approach to analog-to-digital compression Alon Knipis, Yonina Eldar and Andrea Goldsmith
    3. Compressed sensing via compression codes Shirin Jalali and Vincent Poor
    4. Information-theoretic bounds on sketching Mert Pillanci
    5. Sample complexity bounds for dictionary learning from vector- and tensor-valued data Zahra Shakeri, Anand Sarwate and Waheed Bajwa
    6. Uncertainty relations and sparse signal recovery Erwin Riegler and Helmut Bölcskei
    7. Understanding phase transitions via mutual Information and MMSE Galen Reeves and Henry Pfister
    8. Computing choice: learning distributions over permutations Devavrat Shah
    9. Universal clustering Ravi Raman and Lav Varshney
    10. Information-theoretic stability and generalization Maxim Raginsky, Alexander Rakhlin and Aolin Xu
    11. Information bottleneck and representation learning Pablo Piantanida and Leonardo Rey Vega
    12. Fundamental limits in model selection for modern data analysis Jie Ding, Yuhong Yang and Vahid Tarokh
    13. Statistical problems with planted structures: information-theoretical and computational limits Yihong Wu and Jiaming Xu
    14. Distributed statistical inference with compressed data Wenwen Zhao and Lifeng Lai
    15. Network functional compression Soheil Feizi and Muriel Médard
    16. An introductory guide to Fano's inequality with applications in statistical estimation Jonathan Scarlett and Volkan Cevher.

  • Editors

    Miguel R. D. Rodrigues, University College London
    Miguel R. D. Rodrigues is a Reader in Information Theory and Processing in the Department of Electronic and Electrical Engineering, University College London, and a Faculty Fellow at the Turing Institute, London.

    Yonina C. Eldar, Weizmann Institute of Science, Israel
    Yonina C. Eldar is a Professor in the Faculty of Mathematics and Computer Science at the Weizmann Institute of Science, a Fellow of the IEEE and Eurasip, and a member of the Israel Academy of Sciences and Humanities. She is the author of Sampling Theory (Cambridge, 2015), and co-editor of Convex Optimization in Signal Processing and Communications (Cambridge, 2009), and Compressed Sensing (Cambridge, 2012).

    Contributors

    Miguel Rodrigues, Stark Draper, Waheed Bajwa, Yonina Eldar, Alon Knipis, Andrea Goldsmith, Shirin Jalali, Vincent Poor, Mert Pillanci, Zahra Shakeri, Anand Sarwate, Erwin Riegler, Helmut Bölcskei, Galen Reeves, Henry Pfister, Devavrat Shah, Ravi Raman, Lav Varshney, Maxim Raginsky, Alexander Rakhlin, Aolin Xu, Pablo Piantanida, Leonardo Rey Vega, Jie Ding, Yuhong Yang, Vahid Tarokh, Yihong Wu, Jiaming Xu, Wenwen Zhao, Lifeng Lai, Soheil Feizi, Muriel Médard, Jonathan Scarlett, Volkan Cevher

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