| 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 | 1736 | 2016 |
| Minimizing finite sums with the stochastic average gradient M Schmidt, N Le Roux, F Bach Mathematical Programming (MAPR), 2017 | 1620 | 2017 |
| 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 | 1166 | 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 | 757 | 2011 |
| 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 | 513 | 2007 |
| Hybrid deterministic-stochastic methods for data fitting MP Friedlander, M Schmidt SIAM Journal on Scientific Computing (SISC), 2012 | 498 | 2012 |
| Fast patch-based style transfer of arbitrary style TQ Chen, M Schmidt NeurIPS Workshop on Constructive Machine Learning, 2016 | 490 | 2016 |
| Block-coordinate Frank-Wolfe optimization for structural SVMs S Lacoste-Julien, M Jaggi, M Schmidt, P Pletscher International Conference on Machine Learning (ICML), 2013 | 469 | 2013 |
| 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 | 418 | 2006 |
| Fast and faster convergence of SGD for over-parameterized models and an accelerated perceptron S Vaswani, F Bach, M Schmidt International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 | 415 | 2019 |
| 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 | 406 | 2014 |
| 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 | 344 | 2009 |
| Online Learning Rate Adaptation with Hypergradient Descent AG Baydin, R Cornish, DM Rubio, M Schmidt, F Wood International Conference on Learning Representations (ICLR), 2018 | 334 | 2018 |
| 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 | 307 | 2012 |
| Learning graphical model structure using L1-regularization paths M Schmidt, A Niculescu-Mizil, K Murphy National Conference on Artificial Intelligence (AAAI), 2007 | 305 | 2007 |
| 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 | 304 | 2015 |
| Where are the blobs: Counting by Localization with Point Supervision I Hadj Laradji, N Rostamzadeh, P Pinheiro, D Vazquez, M Schmidt European Conference on Computer Vision (ECCV), 2018 | 283 | 2018 |
| Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates S Vaswani, A Mishkin, I Laradji, M Schmidt, G Gidel, S Lacoste-Julien Advances in Neural Information Processing Systems (NeurIPS), 2019 | 281 | 2019 |
| 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 | 272 | 2010 |
| minFunc: unconstrained differentiable multivariate optimization in Matlab M Schmidt http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html, 2005 | 272* | 2005 |
Issam H. LaradjiRS at ServiceNow & Adjunct Prof. at UBC & Founder + AI Impact Catalyst of VAM!Verified email at servicenow.com
