2 citations · 2 across the 2 of their papers we have counts for
3 papers
math.OC2019★ 2 cited
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training
Paul Grigas, Alfonso Lobos, Nathan Vermeersch
The Frank-Wolfe method and its extensions are well-suited for delivering solutions with desirable structural properties, such as sparsity or low-rank structure. We introduce a new…
math.OC2017
Profit Maximization for Online Advertising Demand-Side Platforms
Paul Grigas, Alfonso Lobos, Zheng Wen +1
We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its own profit while acquiring val…
math.ST2015
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…