Information-Theoretic Methods in Data Science

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.

Look Inside

• Copyright Information Page (45 KB)
• Marketing Excerpt (160 KB)
• Index (95 KB)
• Table of Contents (67 KB)
• Front Matter (217 KB)