Reading Material

Prof. Frank Kschiscang’s tutorial

1.  Lattice Coding for Signals and Networks by Ram Zamir (Cambridge University Press, 2014).
2.  C. Feng, D. Silva, and F. R. Kschischang, “An Algebraic Approach to Physical-Layer Network Coding,” IEEE Trans. Info. Theory, vol. 59, pp. 7576-7596, Nov. 2013; doi: https://doi.org/10.1109/TIT.2013.227426
3.  N. E. Tunali, Y.-C. Huang, J. J. Boutros, and K. R. Narayanan, “Lattices over Eisenstein Integers for Compute-and-Forward,” IEEE Trans. Info. Theory, vol. 61, pp. 5306-5321, Oct. 2015; doi:https://doi.org/10.1109/TIT.2015.2451623

Prof. Rajesh Sundaresan’s tutorial

1. M. Mezard and A. Montanari, 2009. Chapter 14 on “Belief Propagation”. In Information, physics, and computation (pp. 291-326). Oxford University Press.
2. Aldous, D. and Steele, J.M., 2004. “The objective method: probabilistic combinatorial optimization and local weak convergence”. In Probability on discrete structures (pp. 1-72). Springer, Berlin, Heidelberg.

Prof. Naftali Tishby’s tutorial

1. The information bottleneck Method https://arxiv.org/pdf/physics/0004057.pdf
2. The Gaussian Information Bottleneck http://www.jmlr.org/papers/volume6/chechik05a/chechik05a.pdf
3. Deep learning and the Information Bottleneck principle https://arxiv.org/pdf/1503.02406.pdf
4. Opening the black box of Deep Learning using Information https://arxiv.org/pdf/1703.00810.pdf
5. The multi-levelinformation bottleneck https://arxiv.org/pdf/1711.05102.pdf
6. Link to the Yandex talks https://youtu.be/bLqJHjXihK8https://youtu.be/RKvS958AqGY
7. Link to the Stanford talk https://youtu.be/XL07WEc2TRI
8. Links to the Simons talks https://youtu.be/EQTtBRM0sIs
9. Related ICLR papers https://arxiv.org/pdf/1612.00410.pdf, https://arxiv.org/pdf/1611.01353.pdf, https://openreview.net/forum?id=ry_WPG-A-

Machine Learning references: