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Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression.
Bradley, S., Hax, A., Magnanti, T.: Applied mathematical programming (1977)
1977
Earlier work this paper cites.
Tikhonov, N., Arsenin., Y.: Solution of ill-posed problems. Winston and Sons. (1977)
1977
Earlier work this paper cites.
Dixit, A.K.: Optimization in economic theory. Oxford University Press on Demand (1990)
1990
Earlier work this paper cites.
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58
1996
Earlier work this paper cites.
Riezler, S., Vasserman, A.: Incremental feature selection and l1 regularization for relaxed maximum-entropy modeling. In: Empirical methods in natural language processing (2004)
2004
Earlier work this paper cites.
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology) (2005)
2005
Earlier work this paper cites.
Andrew, G., Gao, J.: Scalable training of l 1 l_{1} -regularized log-linear models. In: Proceedings of the 24th international conference on Machine learning. pp. 33–40. ACM (2007)
2007
Earlier work this paper cites.
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences 2
2009
Earlier work this paper cites.
Duchi, J., Singer, Y.: Efficient online and batch learning using forward backward splitting. Journal of Machine Learning Research 10
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Master’s thesis, Department of Computer Science, University of Toronto (2009)
2009
Earlier work this paper cites.
Nesterov, Y.: Primal-dual subgradient methods for convex problems. Mathematical programming (2009)
2009
Earlier work this paper cites.
Xiao, L.: Dual averaging methods for regularized stochastic learning and online optimization. Journal of Machine Learning Research 11
2010
Cited alongside, same era.
Sra, S.: Fast projections onto ℓ 1 , q \ell_{1,q} -norm balls for grouped feature selection. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (2011)
2011
Cited alongside, same era.
Lee, J., Sun, Y., Saunders, M.: Proximal newton-type methods for convex optimization. In: Advances in Neural Information Processing Systems. pp. 836–844 (2012)
2012
Cited alongside, same era.
Yuan, G.X., Ho, C.H., Lin, C.J.: An improved glmnet for l1-regularized logistic regression. The Journal of Machine Learning Research 13
2012
Cited alongside, same era.
Johnson, R., Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction. In: Advances in neural information processing systems. pp. 315–323 (2013)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2016)
2016
Later among the works it cites.
Chen, T., Curtis, F.E., Robinson, D.P.: A reduced-space algorithm for minimizing ℓ 1 \ell_{1} -regularized convex functions. SIAM Journal on Optimization 27
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
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2013
Cited alongside, same era.
2013
Cited alongside, same era.
Xiao, L., Zhang, T.: A proximal stochastic gradient method with progressive variance reduction. SIAM Journal on Optimization 24
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Ge, R., Huang, F., Jin, C., Yuan, Y.: Escaping from saddle points—online stochastic gradient for tensor decomposition. In: Conference on Learning Theory. pp. 797–842 (2015)
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms (2017)
2017
Later among the works it cites.
Zhong, K., Song, Z., Jain, P., Bartlett, P.L., Dhillon, I.S.: Recovery guarantees for one-hidden-layer neural networks. In: International Conference on Machine Learning (2017)
2017
Later among the works it cites.
Chen, T.: A Fast Reduced-Space Algorithmic Framework for Sparse Optimization. Ph.D. thesis, Johns Hopkins University (2018)
2018
Later among the works it cites.
Chen, T., Curtis, F.E., Robinson, D.P.: Farsa for ℓ 1 \ell_{1} -regularized convex optimization: local convergence and numerical experience. Optimization Methods and Software (2018)
2018
Later among the works it cites.
Defazio, A., Bottou, L.: On the ineffectiveness of variance reduced optimization for deep learning. In: Advances in Neural Information Processing Systems (2019)
2019
Later among the works it cites.