Mark Schmidt

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	All	Since 2016
Citations	8685	5766
h-index	40	35
i10-index	60	54

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200620072008200920102011201220132014201520162017201820192020202127 43 79 131 201 251 349 487 572 663 899 994 1110 1248 1474 32

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Kevin MurphyResearch Scientist, GoogleVerified email at google.com
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Issam H. LaradjiMcGill + Element AIVerified email at mail.mcgill.ca
Nicolas Le RouxGoogle BrainVerified email at google.com
Julie NutiniUniversity of British ColumbiaVerified email at cs.ubc.ca
Sharan VaswaniUniversity of AlbertaVerified email at ualberta.ca
Michael P FriedlanderIBM Professor of Computational Mathematics, University of British ColumbiaVerified email at cs.ubc.ca
Russ GreinerProfessor of Computer Science, University of AlbertaVerified email at ualberta.ca
Romer RosalesLinkedInVerified email at linkedin.com
Glenn FungAmerican Family InsuranceVerified email at amfam.com
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Alireza ShafaeiUniversity of British ColumbiaVerified email at cs.ubc.ca
Patrick PletscherMachine Learning ScientistVerified email at pletscher.org
Martin JaggiEPFLVerified email at epfl.ch
Mohamed Osama AhmedUniversity of British ColumbiaVerified email at cs.ubc.ca
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityVerified email at stat.purdue.edu
Jörg SanderProfessor, Computing Science, University of AlbertaVerified email at ualberta.ca
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerified email at postman.riken.jp
Ewout van den BergIBM T.J. Watson Research CenterVerified email at us.ibm.com
Hoyt KoepkeUniversity of Washington Department of StatisticsVerified email at stat.washington.edu

Mark Schmidt

Associate Professor of Computer Science, University of British Columbia

Verified email at cs.ubc.ca - Homepage

Machine Learning Numerical Optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Minimizing finite sums with the stochastic average gradient M Schmidt, N Le Roux, F Bach Mathematical Programming (MAPR), 2017, 2013	869*	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	719	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	445	2011
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	429	2016
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	395	2006
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	368	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	363	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	285	2014
Hybrid deterministic-stochastic methods for data fitting MP Friedlander, M Schmidt SIAM Journal on Scientific Computing (SISC), 2012	279	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	263	2009
Learning graphical model structure using L1-regularization paths M Schmidt, A Niculescu-Mizil, K Murphy National Conference on Artificial Intelligence (AAAI), 2007	237	2007
minFunc: unconstrained differentiable multivariate optimization in Matlab M Schmidt http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html, 2005	223*	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	208	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	192	2005
Fast patch-based style transfer of arbitrary style TQ Chen, M Schmidt NeurIPS Workshop on Constructive Machine Learning, 2016	191	2016
Least squares optimization with l1-norm regularization M Schmidt CPSC 542B Course Project Report, 2005	182	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	178	2008
Graphical model structure learning with l1-regularization M Schmidt Ph.D. Thesis, University of British Columbia, 2010	172	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	169	2015
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	155	2012

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