Stochastics and Statistics Seminar Series Marco Mondelli, Institute of Science and Technology Austria
Inference in High Dimensions for (Mixed) Generalized Linear Models: the Linear, the Spectral and the Approximate
Abstract: In a generalized linear model (GLM), the goal is to estimate a d-dimensional signal x from an n-dimensional observation of the form f(Ax, w), where A is a design matrix and w is a noise vector. Well-known examples of GLMs include linear regression, phase retrieval, 1-bit compressed sensing, and logistic regression. We focus on…
