Search Results for author:
Found 6 papers, 1 papers with code
Structured Transforms Across Spaces with Cost-Regularized Optimal Transport
Matching a source to a target probability measure is often solved by instantiating a linear optimal transport (OT) problem, parameterized by a ground cost function that quantifies discrepancy between points.
Randomized Stochastic Gradient Descent Ascent
RSGDA can be parameterized using optimal loop sizes that guarantee the best convergence rates known to hold for SGDA.
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization
We showcase this by obtaining a simple formula for the optimal minibatch size of two variance reduced methods (\textit{L-SVRG} and \textit{SAGA}).
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
Stochastic Gradient Descent (SGD) is being used routinely for optimizing non-convex functions.
Almost sure convergence rates for Stochastic Gradient Descent and Stochastic Heavy Ball
We show that these results still hold when using stochastic line search and stochastic Polyak stepsizes, thereby giving the first proof of convergence of these methods in the non-overparametrized regime.
Towards closing the gap between the theory and practice of SVRG
Among the very first variance reduced stochastic methods for solving the empirical risk minimization problem was the SVRG method (Johnson & Zhang 2013).
