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All publications by Mark Schmidt
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Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection
Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander and Hoyt Koepke
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015


StopWasting My Gradients: Practical SVRG
Reza Harikandeh, Mohamed O. Ahmed, Alim Virani, Mark Schmidt, Jakub Kone\vcn\'y and Scott Sallinen
Advances in Neural Information Processing Systems 28, 2015


Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
Simon Lacoste-julien, Martin Jaggi, Mark Schmidt and Patrick Pletscher
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets
Nicolas L. Roux, Mark Schmidt and Francis R. Bach
Advances in Neural Information Processing Systems 25, 2012


Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
Mark Schmidt, Nicolas L. Roux and Francis R. Bach
Advances in Neural Information Processing Systems 24, 2011


An interior-point stochastic approximation method and an L1-regularized delta rule
Peter Carbonetto, Mark Schmidt and Nando D. Freitas
Advances in Neural Information Processing Systems 21, 2008