Mark Schmidt
Mark Schmidt
Associate Professor of Computer Science, University of British Columbia
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Minimizing finite sums with the stochastic average gradient
M Schmidt, N Le Roux, F Bach
Mathematical Programming (MAPR), 2017, 2013
A stochastic gradient method with an exponential convergence rate for finite training sets
N Le Roux, M Schmidt, FR Bach
Advances in Neural Information Processing Systems (NeurIPS), 2012
Convergence rates of inexact proximal-gradient methods for convex optimization
M Schmidt, N Le Roux, FR Bach
Advances in Neural Information Processing Systems (NeurIPS), 2011
Accelerated training of conditional random fields with stochastic gradient methods
SVN Vishwanathan, NN Schraudolph, MW Schmidt, KP Murphy
International Conference on Machine Learning (ICML), 2006
Linear Convergence of Gradient and Proximal-Gradient Methods under the Polyak-Łojasiewicz Condition
H Karimi, J Nutini, M Schmidt
European Conference on Machine Learning (ECML), 2016
Fast optimization methods for l1 regularization: A comparative study and two new approaches
M Schmidt, G Fung, R Rosales
European Conference on Machine Learning (ECML), 2007
Block-coordinate Frank-Wolfe optimization for structural SVMs
S Lacoste-Julien, M Jaggi, M Schmidt, P Pletscher
International Conference on Machine Learning (ICML), 2013
Convex optimization for big data: Scalable, randomized, and parallel algorithms for big data analytics
V Cevher, S Becker, M Schmidt
IEEE Signal Processing Magazine, 2014
Hybrid deterministic-stochastic methods for data fitting
MP Friedlander, M Schmidt
SIAM Journal on Scientific Computing (SISC), 2012
Optimizing costly functions with simple constraints: A limited-memory projected quasi-newton algorithm
MW Schmidt, E Berg, MP Friedlander, KP Murphy
International Conference on Artificial Intelligence and Statistics (AISTATS), 2009
Learning graphical model structure using L1-regularization paths
M Schmidt, A Niculescu-Mizil, K Murphy
National Conference on Artificial Intelligence (AAAI), 2007
minFunc: unconstrained differentiable multivariate optimization in Matlab
M Schmidt, 2005
Modeling annotator expertise: Learning when everybody knows a bit of something
Y Yan, R Rosales, G Fung, MW Schmidt, GH Valadez, L Bogoni, L Moy, ...
International Conference on Artificial Intelligence and Statistics (AISTATS), 2010
Segmenting brain tumors with conditional random fields and support vector machines
CH Lee, M Schmidt, A Murtha, A Bistritz, J Sander, R Greiner
Computer vision for biomedical image applications (CVBIA), 2005
Method and System for Automatic Detection and Segmentation of Tumors and Associated Edema (Swelling) in Magnetic Resonance (Mri) Images
M Schmidt, R Greiner, AD Murtha
US Patent App. 11/912,864, 2008
Least squares optimization with l1-norm regularization
M Schmidt
CPSC 542B Course Project Report, 2005
Fast patch-based style transfer of arbitrary style
TQ Chen, M Schmidt
NeurIPS Workshop on Constructive Machine Learning, 2016
Graphical model structure learning with l1-regularization
M Schmidt
Ph.D. Thesis, University of British Columbia, 2010
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection
J Nutini, M Schmidt, IH Laradji, M Friedlander, H Koepke
International Conference on Machine Learning (ICML), 2015
3D variational brain tumor segmentation using a high dimensional feature set
D Cobzas, N Birkbeck, M Schmidt, M Jagersand, A Murtha
Mathematical Methods in Biomedical Image Analysis (MMBIA), 2007
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