research
∙
12/31/2020
Constrained and Composite Optimization via Adaptive Sampling Methods
The motivation for this paper stems from the desire to develop an adapti...
share
research
∙
02/15/2018
A Progressive Batching L-BFGS Method for Machine Learning
The standard L-BFGS method relies on gradient approximations that are no...
research
∙
10/30/2017
Adaptive Sampling Strategies for Stochastic Optimization
In this paper, we propose a stochastic optimization method that adaptive...
research
∙
05/17/2017
An Investigation of Newton-Sketch and Subsampled Newton Methods
The concepts of sketching and subsampling have recently received much at...
research
∙
09/27/2016
Exact and Inexact Subsampled Newton Methods for Optimization
The paper studies the solution of stochastic optimization problems in wh...
research
∙
06/15/2016
Optimization Methods for Large-Scale Machine Learning
This paper provides a review and commentary on the past, present, and fu...
research
∙
05/19/2016