33 citations · 61 across the 3 of their papers we have counts for
3 papers
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…
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…
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method
Simon Lacoste-Julien, Mark Schmidt, Francis Bach
In this note, we present a new averaging technique for the projected stochastic subgradient method. By using a weighted average with a weight of t+1 for each iterate w_t at iterati…