70 citations · 445 across the 17 of their papers we have counts for
9 papers · 1 filter
Inexact Non-Convex Newton-Type Methods
Zhewei Yao, Peng Xu, Farbod Roosta-Khorasani +1
For solving large-scale non-convex problems, we propose inexact variants of trust region and adaptive cubic regularization methods, which, to increase efficiency, incorporate vario…
Optimization Methods for Inverse Problems
Nan Ye, Farbod Roosta-Khorasani, Tiangang Cui
Optimization plays an important role in solving many inverse problems. Indeed, the task of inversion often either involves or is fully cast as a solution of an optimization problem…
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…
Sub-sampled Newton Methods with Non-uniform Sampling
Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani +2
We consider the problem of finding the minimizer of a convex function of the form where a low-rank facto…
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
Xiang Cheng, Farbod Roosta-Khorasani, Stefan Palombo +2
We consider first order gradient methods for effectively optimizing a composite objective in the form of a sum of smooth and, potentially, non-smooth functions. We present accelera…
Sub-Sampled Newton Methods II: Local Convergence Rates
Farbod Roosta-Khorasani, Michael W. Mahoney
Many data-fitting applications require the solution of an optimization problem involving a sum of large number of functions of high dimensional parameter. Here, we consider the pro…
Sub-Sampled Newton Methods I: Globally Convergent Algorithms
Farbod Roosta-Khorasani, Michael W. Mahoney
Large scale optimization problems are ubiquitous in machine learning and data analysis and there is a plethora of algorithms for solving such problems. Many of these algorithms emp…
Variational Perspective on Local Graph Clustering
Kimon Fountoulakis, Farbod Roosta-Khorasan, Julian Shun +2
Modern graph clustering applications require the analysis of large graphs and this can be computationally expensive. In this regard, local spectral graph clustering methods aim to…