Information-Theoretic Methods in Data Science (eBook, PDF)

Redaktion: Rodrigues, Miguel R. D.

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 29. Februar 2020
• Englisch
• ISBN-13: 9781108606530
• Artikelnr.: 66178723

Inhaltsangabe

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.