538 citations · 2.3k across the 54 of their papers we have counts for
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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…
Group Sparse Priors for Covariance Estimation
Benjamin Marlin, Mark Schmidt, Kevin Murphy
Recently it has become popular to learn sparse Gaussian graphical models (GGMs) by imposing l1 or group l1,2 penalties on the elements of the precision matrix. Thispenalized likeli…
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
Mark Schmidt, Kevin Murphy
We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to…
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
Nicolas Le Roux, Mark Schmidt, Francis Bach
We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is strongly convex. While standard stochastic gradient methods…