259 citations · 2.9k across the 123 of their papers we have counts for
5 papers · 2 filters
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…