605 citations · 4k across the 143 of their papers we have counts for
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Efficient Distributed SGD with Variance Reduction
Soham De, Tom Goldstein
Stochastic Gradient Descent (SGD) has become one of the most popular optimization methods for training machine learning models on massive datasets. However, SGD suffers from two ma…
Variance Reduction for Distributed Stochastic Gradient Descent
Soham De, Gavin Taylor, Tom Goldstein
Variance reduction (VR) methods boost the performance of stochastic gradient descent (SGD) by enabling the use of larger, constant stepsizes and preserving linear convergence rates…
Self-Expressive Decompositions for Matrix Approximation and Clustering
Eva L. Dyer, Tom A. Goldstein, Raajen Patel +2
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure…
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction
Tom Goldstein, Gavin Taylor, Kawika Barabin +1
Recent approaches to distributed model fitting rely heavily on consensus ADMM, where each node solves small sub-problems using only local data. We propose iterative methods that so…
FASTA: A Generalized Implementation of Forward-Backward Splitting
Tom Goldstein, Christoph Studer, Richard Baraniuk
This is a user manual for the software package FASTA.