538 citations · 2.3k across the 54 of their papers we have counts for
6 papers · 1 filter
Stop Wasting My Gradients: Practical SVRG
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani +3
We present and analyze several strategies for improving the performance of stochastic variance-reduced gradient (SVRG) methods. We first show that the convergence rate of these met…
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin +2
Several recent works have explored stochastic gradient methods for variational inference that exploit the geometry of the variational-parameter space. However, the theoretical prop…
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection
Julie Nutini, Mark Schmidt, Issam H. Laradji +2
There has been significant recent work on the theory and application of randomized coordinate descent algorithms, beginning with the work of Nesterov [SIAM J. Optim., 22(2), 2012],…
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields
Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed +3
We apply stochastic average gradient (SAG) algorithms for training conditional random fields (CRFs). We describe a practical implementation that uses structure in the CRF gradient…
Influence Maximization with Bandits
Sharan Vaswani, Laks. V. S. Lakshmanan, Mark Schmidt
We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to…
Hierarchical Maximum-Margin Clustering
Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt +2
We present a hierarchical maximum-margin clustering method for unsupervised data analysis. Our method extends beyond flat maximum-margin clustering, and performs clustering recursi…