Mark Schmidt
Mark Schmidt
Associate Professor of Computer Science, University of British Columbia
Zweryfikowany adres z cs.ubc.ca - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Minimizing finite sums with the stochastic average gradient
M Schmidt, N Le Roux, F Bach
Mathematical Programming (MAPR), 2017, 2013
1043*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
8012012
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
5352016
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
4952011
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
4082006
Block-coordinate Frank-Wolfe optimization for structural SVMs
S Lacoste-Julien, M Jaggi, M Schmidt, P Pletscher
International Conference on Machine Learning (ICML), 2013
3862013
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
3852007
Hybrid deterministic-stochastic methods for data fitting
MP Friedlander, M Schmidt
SIAM Journal on Scientific Computing (SISC), 2012
3182012
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
2932014
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
2792009
Learning graphical model structure using L1-regularization paths
M Schmidt, A Niculescu-Mizil, K Murphy
National Conference on Artificial Intelligence (AAAI), 2007
2492007
minFunc: unconstrained differentiable multivariate optimization in Matlab
M Schmidt
http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html, 2005
229*2005
Fast patch-based style transfer of arbitrary style
TQ Chen, M Schmidt
NeurIPS Workshop on Constructive Machine Learning, 2016
2282016
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
2192010
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
1982015
Least squares optimization with l1-norm regularization
M Schmidt
CPSC 542B Course Project Report, 2005
1972005
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
1972005
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method
S Lacoste-Julien, M Schmidt, F Bach
arXiv preprint arXiv:1212.2002, 2012
1902012
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
1862008
Graphical model structure learning with l1-regularization
M Schmidt
Ph.D. Thesis, University of British Columbia, 2010
1782010
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