Statistical Estimation: From Denoising to Sparse Regression and Hidden Cliques
Abstract
These notes review six lectures given by Prof. Andrea Montanari on the topic of statistical estimation for linear models. The first two lectures cover the principles of signal recovery from linear measurements in terms of minimax risk. Subsequent lectures demonstrate the application of these principles to several practical problems in science and engineering. Specifically, these topics include denoising of error-laden signals, recovery of compressively sensed signals, reconstruction of low-rank matrices, and also the discovery of hidden cliques within large networks.
Keywords
Cite
@article{arxiv.1409.5557,
title = {Statistical Estimation: From Denoising to Sparse Regression and Hidden Cliques},
author = {Eric W. Tramel and Santhosh Kumar and Andrei Giurgiu and Andrea Montanari},
journal= {arXiv preprint arXiv:1409.5557},
year = {2014}
}
Comments
Chapter of "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", Eds.: F. Krzakala, F. Ricci-Tersenghi, L. Zdeborova, R. Zecchina, E. W. Tramel, L. F. Cugliandolo (Oxford University Press, to appear)