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Our work focuses on stochastic gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer.
The MNIST database of handwritten digits
LeCun, Y · 1998
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Variable selection via nonconcave penalized likelihood and its oracle properties
Fan, J. and Li, R · 2001
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On ψ \psi -learning
Shen, X., Tseng, G. C., Zhang, X., and Wong, W. H · 2003
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Robust truncated hinge loss support vector machines
Wu, Y. and Liu, Y · 2007
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Enhancing sparsity by reweighted ł 1 \l_{1} minimization
Candes, E. J., Wakin, M. B., and Boyd, S. P · 2008
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Tighter bounds for structured estimation
Chapelle, O., Do, C. B., Teo, C. H., Le, Q. V., and Smola, A. J · 2009
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On the design of loss functions for classification: theory, robustness to outliers, and savageboost
Masnadi-Shirazi, H. and Vasconcelos, N · 2009
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Variational analysis , volume 317
Rockafellar, R. T. and Wets, R. J.-B · 2009
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On the design of robust classifiers for computer vision
Masnadi-Shirazi, H., Mahadevan, V., and Vasconcelos, N · 2010
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Nearly unbiased variable selection under minimax concave penalty
Zhang, C.-H · 2010
Cited alongside, same era.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. and Lan, G · 2013
Cited alongside, same era.
A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems
Gong, P., Zhang, C., Lu, Z., Huang, J., and Ye, J · 2013
Cited alongside, same era.
Accelerating stochastic gradient descent using predictive variance reduction
Johnson, R. and Zhang, T · 2013
Cited alongside, same era.
Understanding machine learning: From theory to algorithms
Shalev-Shwartz, S. and Ben-David, S · 2014
Cited alongside, same era.
A proximal stochastic gradient method with progressive variance reduction
Feature selection with annealing for computer vision and big data learning
Barbu, A., She, Y., Ding, L., and Gramajo, G · 2017
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First-order methods in optimization, volume 25 of MOS-SIAM Series on Optimization
Beck, A · 2017
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Liu, T., Pong, T. K., and Takeda, A · 2017
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Stochastic difference of convex algorithm and its application to training deep boltzmann machines
Nitanda, A. and Suzuki, T · 2017
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a9a dataset
Fan, R.-E · 2018
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Stochastic Gradient Descent for Stochastic Doubly-Nonconvex Composite Optimization
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Xiao, L. and Zhang, T · 2014
Cited alongside, same era.
Variance reduction for faster non-convex optimization
Allen-Zhu, Z. and Hazan, E · 2016
Cited alongside, same era.
Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
Ghadimi, S., Lan, G., and Zhang, H · 2016
Cited alongside, same era.
Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization
Reddi, S. J., Sra, S., Póczos, B., and Smola, A. J · 2016
Cited alongside, same era.
Kawashima, T. and Fujisawa, H · 2018
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A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Li, Z. and Li, J · 2018
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Xu, Y., Qi, Q., Lin, Q., Jin, R., and Yang, T · 2018
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Xu, Y., Qi, Q., Lin, Q., Jin, R., and Yang, T · 2019
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