Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods

Neyman Seminar
Nov 14, 2018 4:00pm to 5:00pm
Location: 
1011 Evans Hall
Status: 
Happening As Scheduled
Logistic regression is one of the most popular methods in binary classification, wherein estimation of model parameters is carried out by solving the maximum likelihood (ML) optimization problem, and the ML estimator is defined to be the optimal solution of this problem. It is well known that the ML estimator exists when the data is non-separable, but fails to exist when the data is separable....
Paul Grigas, UC Berkeley