ELE538B: Sparsity, Structure and Inference

Textbooks. We recommend the following two books, although we will not follow them closely.

• Statistical learning with sparsity: the Lasso and generalizations, Trevor Hastie, Robert Tibshirani, and Martin Wainwright, Chapman and Hall/CRC, 2015.

• A mathematical introduction to compressive sensing, Simon Foucart, and Holger Rauhut, Springer, 2013.

References. The following references also contain topics relevant to this course, and you might want to consult them.

• Mathematics of sparsity (and a few other things), Emmanuel Candes, International Congress of Mathematicians, 2014.

• Sparse and redundant representations: from theory to applications in signal and image processing, Michael Elad, Springer, 2010.

• Graphical models, exponential families, and variational inference, Martin Wainwright, and Michael Jordan, Foundations and Trends in Machine Learning, 2008.

• Introduction to the non-asymptotic analysis of random matrices, Roman Vershynin, Compressed Sensing: Theory and Applications, 2010.

• Convex optimization, Stephen Boyd, and Lieven Vandenberghe, Cambridge University Press, 2004.

• Topics in random matrix theory, Terence Tao, American Mathematical Society, 2012.