69 citations · 235 across the 8 of their papers we have counts for
4 papers · 1 filter
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang, Farbod Roosta-Khorasani, Peng Xu +1
For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At…
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study
Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney
While first-order optimization methods such as stochastic gradient descent (SGD) are popular in machine learning (ML), they come with well-known deficiencies, including relatively-…
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information
Peng Xu, Fred Roosta, Michael W. Mahoney
We consider variants of trust-region and cubic regularization methods for non-convex optimization, in which the Hessian matrix is approximated. Under mild conditions on the inexact…
Accelerated Stochastic Power Iteration
Christopher De Sa, Bryan He, Ioannis Mitliagkas +2
Principal component analysis (PCA) is one of the most powerful tools in machine learning. The simplest method for PCA, the power iteration, requires full-data pas…