260 citations · 1.8k across the 63 of their papers we have counts for
5 papers · 1 filter
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
Lam M. Nguyen, Jie Liu, Katya Scheinberg +1
In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving emp…
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
Lam M. Nguyen, Jie Liu, Katya Scheinberg +1
In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. D…
On Optimal Probabilities in Stochastic Coordinate Descent Methods
Peter Richtárik, Martin Takáč
We propose and analyze a new parallel coordinate descent method---`NSync---in which at each iteration a random subset of coordinates is updated, in parallel, allowing for the subse…
Distributed Coordinate Descent Method for Learning with Big Data
Peter Richtárik, Martin Takáč
In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent method for solving loss minimization problems with big data. We initially partition the coordinates (features)…
Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes
Peter Richtárik, Majid Jahani, Selin Damla Ahipaşaoğlu +1
Given a multivariate data set, sparse principal component analysis (SPCA) aims to extract several linear combinations of the variables that together explain the variance in the dat…