72 citations · 131 across the 4 of their papers we have counts for
4 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…
Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems
Aaron J. Defazio, Tibério S. Caetano, Justin Domke
Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem. In this work…
A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation
Aaron J. Defazio, Tiberio S. Caetano
A key problem in statistics and machine learning is the determination of network structure from data. We consider the case where the structure of the graph to be reconstructed is k…
A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training
Aaron Defazio, Tiberio Caetano
Item neighbourhood methods for collaborative filtering learn a weighted graph over the set of items, where each item is connected to those it is most similar to. The prediction of…