We analyze the stochastic average gradient (SAG) method for optimizing the sum of a finite number of smooth convex functions, which achieves a faster convergence rate than black-box SG methods.Expand

We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex term using proximal-gradient methods, where an error is present in the calculation of the gradient of the smooth term.Expand

We propose a novel randomized block-coordinate version of the Frank-Wolfe algorithm for structural SVMs that achieves an Õ(1/ε) convergence rate while only requiring a single call to the maximization oracle on each iteration.Expand

We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs).Expand

By controlling the sample size in an incremental gradient algorithm, it is possible to maintain the steady convergence rates of full-gradient methods.Expand

L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm.Expand