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We study the problem of minimizing the average of a very large number of smooth functions, which is of key importance in training supervised learning models.
A stochastic approximation method
Robbins, H. and Monro, S · 1951
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Robust stochastic approximation approach to stochastic programming
Nemirovski, A., Juditsky, A., Lan, G., and Shapiro, A · 2009
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Accelerating stochastic gradient descent using predictive variance reduction
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Mini-batch primal and dual methods for SVMs
Takáč, M., Bijral, A., Richtárik, P., and Srebro, N · 2013
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
Defazio, A., Bach, F., and Lacoste-Julien, S · 2014
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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Richtárik, P. and Takáč, M · 2014
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Understanding machine learning: from theory to algorithms
Shalev-Shwartz, S. and Ben-David, S · 2014
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Primal method for ERM with flexible mini-batching schemes and non-convex losses
Csiba, D. and Richtárik, P · 2015
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Incremental majorization-minimization optimization with application to large-scale machine learning
Mairal, J · 2015
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Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
Needell, D., Srebro, N., and Ward, R · 2015
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Qu, Z., Richtárik, P., and Zhang, T · 2015
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Schmidt, M., Babanezhad, R., Ahmed, M. O., Defazio, A., Clifton, A., and Sarkar, A · 2015
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Stochastic primal-dual coordinate method for regularized empirical risk minimization
Zhang, Y. and Lin, X · 2015
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Stochastic optimization with importance sampling
Zhao, P. and Zhang, T · 2015
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Konečný, J., Lu, J., Richtárik, P., and Takáč, M · 2016
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On optimal probabilities in stochastic coordinate descent methods
Richtárik, P. and Takáč, M · 2016
Adaptive svrg methods under error bound conditions with unknown growth parameter
Xu, Y., Lin, Q., and Yang, T · 2017
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Improving SAGA via a probabilistic interpolation with gradient descent
Bibi, A., Sailanbayev, A., Ghanem, B., Gower, R. M., and Richtárik, P · 2018
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Importance sampling for minibatches
Csiba, D. and Richtárik, P · 2018
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Stochastic quasi-gradient methods: Variance reduction via Jacobian sketching
Gower, R. M., Richtárik, P., and Bach, F · 2018
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Accelerated coordinate descent with arbitrary sampling and best rates for minibatches
Hanzely, F. and Richtárik, P · 2018
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Nonconvex variance reduced optimization with arbitrary sampling
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SDCA without duality, regularization, and individual convexity
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Chambolle, A., Ehrhardt, M. J., Richtárik, P., and Schönlieb, C. B · 2017
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Linearly convergent stochastic heavy ball method for minimizing generalization error
Loizou, N. and Richtárik, P
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Horváth, S. and Richtárik, P · 2018
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Linear convergence of first order methods for non-strongly convex optimization
Necoara, I., Nesterov, Y., and Glineur, F · 2018
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Local convergence properties of SAGA/Prox-SVRG and acceleration
Poon, C., Liang, J., and Schönlieb, C · 2018
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Accelerated coordinate descent with arbitrary sampling and best rates for minibatches
Hanzely, F. and Richtárik, P · 2019
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Xiao, L. and Zhang, T · 2075
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