5 papers
An Oblivious Ellipsoid Algorithm for Solving a System of (In)Feasible Linear Inequalities
Jourdain Lamperski, Robert M. Freund, Michael J. Todd
The ellipsoid algorithm is a fundamental algorithm for computing a solution to the system of linear inequalities in variables when its set of solutio…
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods
Robert M. Freund, Paul Grigas, Rahul Mazumder
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) optimi…
Generalized Stochastic Frank-Wolfe Algorithm with Stochastic "Substitute" Gradient for Structured Convex Optimization
Haihao Lu, Robert M. Freund
The stochastic Frank-Wolfe method has recently attracted much general interest in the context of optimization for statistical and machine learning due to its ability to work with a…
Accelerating Greedy Coordinate Descent Methods
Haihao Lu, Robert M. Freund, Vahab Mirrokni
We study ways to accelerate greedy coordinate descent in theory and in practice, where "accelerate" refers either to convergence in theory, in practice, or both. We intr…
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives
Robert M. Freund, Paul Grigas, Rahul Mazumder
In this paper we analyze boosting algorithms in linear regression from a new perspective: that of modern first-order methods in convex optimization. We show that classic boosting a…