The first unified treatment of the interface between information theory and emerging topics in data science.
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 Boelcskei;
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 Medard;
16. An introductory guide to Fano's inequality with applications in statistical estimation Jonathan Scarlett and Volkan Cevher.
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 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).