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The application of stochastic variance reduction to optimization has shown remarkable recent theoretical and practical success.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Stochastic dual coordinate ascent methods for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2013
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Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
Aaron Defazio, Francis Bach, and Simon Lacoste-Julien · 2014
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Finito: A faster, permutable incremental gradient method for big data problems
Aaron Defazio, Tiberio Caetano, and Justin Domke · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Incremental majorization-minimization optimization with application to large-scale machine learning
Julien Mairal · 2014
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Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Competing with the empirical risk minimizer in a single pass
Roy Frostig, Rong Ge, Sham M. Kakade, and Aaron Sidford · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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A universal catalyst for first-order optimization
Hongzhou Lin, Julien Mairal, and Zaid Harchaoui · 2015
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Variance reduction for faster non-convex optimization
Zeyuan Allen-Zhu and Elad Hazan · 2016
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Stochastic variance reduction methods for saddle-point problems
P. Balamurugan and Francis Bach · 2016
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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arne Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2016
Stochastic optimization with variance reduction for infinite datasets with finite sum structure
Alberto Bietti and Julien Mairal · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Less than a Single Pass: Stochastically Controlled Stochastic Gradient
Lihua Lei and Michael Jordan · 2017
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Non-convex finite-sum optimization via scsg methods
Lihua Lei, Cheng Ju, Jianbo Chen, and Michael I Jordan · 2017
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Sarah: A novel method for machine learning problems using stochastic recursive gradient
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A simple practical accelerated method for finite sums
Aaron Defazio · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Stochastic variance reduction for nonconvex optimization
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczós, and Alex Smola · 2016
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Katyusha: The first direct acceleration of stochastic gradient methods
Zeyuan Allen-Zhu · 2017
Cited alongside, same era.
Lam M. Nguyen, Jie Liu, Katya Scheinberg, and Martin Takáč · 2017
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Minimizing finite sums with the stochastic average gradient
Mark Schmidt, Nicolas Le Roux, and Francis Bach · 2017
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On the insufficiency of existing momentum schemes for stochastic optimization
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, and Sham M. Kakade · 2018
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Catalyst for gradient-based nonconvex optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, and Zaid Harchaoui · 2018
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Finite-sum smooth optimization with sarah
Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, and Jayant R. Kalagnanam · 2019
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