33 citations · 113 across the 8 of their papers we have counts for
3 papers · 1 filter
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
The Bayesian learning rule is a natural-gradient variational inference method, which not only contains many existing learning algorithms as special cases but also enables the desig…
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Natural-gradient methods enable fast and simple algorithms for variational inference, but due to computational difficulties, their use is mostly limited to \emph{minimal} exponenti…
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…