Sebastien Bubeck just came out with a monograph on the Theory of Convex Optimization for Machine Learning while Roman Vershynin just released the following preprint:
Estimation in high dimensions: a geometric perspective
This tutorial paper provides an exposition of a flexible geometric framework for high dimensional estimation problems with constraints. The paper develops geometric intuition about high dimensional sets, justifies it with some results of asymptotic convex geometry, and demonstrates connections between geometric results and estimation problems. The theory is illustrated with applications to sparse recovery, matrix completion, quantization, linear and logistic regression and generalized linear models.
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