Convex Gaussian Min-max theorem

On Optimal Regularization in Estimation, Detection, and Classification

Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

Apr 30, 12:00 - 13:00

KAUST

linear systems computational Complexity Convex Gaussian Min-max theorem

In many problems in statistical signal processing, regularization is employed to deal with uncertainty, ill-posedness, and insufficiency of training data. It is possible to tune these regularizers optimally asymptotically, i.e. when the dimension of the problem becomes very large, by using tools from random matrix theory and Gauss Process Theory. In this talk, we demonstrate the optimal turning of regularization for three problems : i) Regularized least squares for solving ill-posed and/or uncertain linear systems, 2) Regularized least squares for signal detection in multiple antenna communication systems and 3) Regularized linear and quadratic discriminant binary classifiers.

Professor Tareq Al-Naffouri Talk on On Optimal Regularization in Estimation, Detection, and Classification

1 min read · Tue, Jul 28 2020

linear systems computational theory Convex Gaussian Min-max theorem

On April 30th 2020, Professor Tareq Al-Naffouri delivered a talk on Optimal Regularization in Estimation, Detection, and Classification. Due to the COVID-19 pandemic, the talk was delivered online via Zoom. To find the abstract of the talk, press here.