Fetching the paper…
Reading the bibliography…
This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems.
M. V. Solodov, “Incremental gradient algorithms with stepsizes bounded away from zero,” Computational Optimization and Applications , vol. 11, no. 1, pp. 23–35, 1998
1998
Earlier work this paper cites.
D. D. Lewis, Y. Yang, T. G. Rose, and F. Li, “Rcv1: A new benchmark collection for text categorization research,” J. Mach. Learn. Res. , vol. 5, pp. 361–397, Dec. 2004. [Online]. Available: http://dl.acm.org/citation.cfm?id=1005332.1005345
2004
Earlier work this paper cites.
D. Blatt, A. O. Hero, and H. Gauchman, “A convergent incremental gradient method with a constant step size,” SIAM J. Optim. , vol. 18, no. 1, pp. 29–51, jan 2007. [Online]. Available: http://dx.doi.org/10.1137/040615961
2007
Earlier work this paper cites.
J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Identifying suspicious urls: An application of large-scale online learning,” in Proceedings of the 26th Annual International Conference on Machine Learning (ICML 2009) , ser. ICML ’09, ACM. Montreal, Quebec: ACM, June 2009, pp. 681–688. [Online]. Available: http://doi.acm.org/10.1145/1553374.1553462
2009
Earlier work this paper cites.
B. Recht, C. Re, S. Wright, and F. Niu, “Hogwild: A lock-free approach to parallelizing stochastic gradient descent,” in Advances in Neural Information Processing Systems 24 , 2011, pp. 693–701
2011
Earlier work this paper cites.
A. Agarwal and J. C. Duchi, “Distributed delayed stochastic optimization,” in Advances in Neural Information Processing Systems , 2011, pp. 873–881
2011
Earlier work this paper cites.
D. P. Bertsekas, “Incremental gradient, subgradient, and proximal methods for convex optimization: A survey,” Optimization for Machine Learning , vol. 2010, pp. 1–38, 2011
2011
Cited alongside, same era.
Y. Nesterov, “Efficiency of coordinate descent methods on huge-scale optimization problems,” SIAM Journal on Optimization , vol. 22, no. 2, pp. 341–362, 2012
2012
Cited alongside, same era.
O. Dekel, R. Gilad-Bachrach, O. Shamir, and L. Xiao, “Optimal distributed online prediction using mini-batches,” Journal of Machine Learning Research , vol. 13, pp. 165–202, 2012
2012
Cited alongside, same era.
M. Li, L. Zhou, Z. Yang, A. Li, F. Xia, D. G. Andersen, and A. Smola, “Parameter server for distributed machine learning,” in Big Learning NIPS Workshop , vol. 1, 2013
2013
Cited alongside, same era.
M. Gurbuzbalaban, A. Ozdaglar, and P. Parrilo, “On the convergence rate of incremental aggregated gradient algorithms,” jun 2015, arXiv: 1506.02081v1 [math.OC]
2015
Later among the works it cites.
J. Mairal, “Incremental majorization-minimization optimization with application to large-scale machine learning,” SIAM Journal on Optimization , vol. 25, no. 2, pp. 829–855, 2015
2015
Later among the works it cites.
H. R. Feyzmahdavian, A. Aytekin, and M. Johansson, “An asynchronous mini-batch algorithm for regularized stochastic optimization,” IEEE Transactions on Automatic Control , vol. PP, no. 99, pp. 1–1, 2016
2016
Closest in time.
N. D. Vanli, M. Gurbuzbalaban, and A. Ozdaglar, “Global convergence rate of proximal incremental aggregated gradient methods,” aug 2016, arXiv: 1608.01713v1 [math.OC]
2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Defazio, F. Bach, and S. Lacoste-Julien, “Saga: A fast incremental gradient method with support for non-strongly convex composite objectives,” in Advances in Neural Information Processing Systems 27 . Curran Associates, Inc., 2014, pp. 1646–1654. [Online]. Available: http://papers.nips.cc/paper/5258-saga-a-fast-incremental-gradient-method-with-support-for-non-strongly-convex-composite-objectives.pdf
2014
Cited alongside, same era.
L. Xiao and T. Zhang, “A proximal stochastic gradient method with progressive variance reduction,” SIAM Journal on Optimization , vol. 24, no. 4, pp. 2057–2075, 2014. [Online]. Available: http://dx.doi.org/10.1137/140961791
2014
Cited alongside, same era.
M. Schmidt, N. Le Roux, and F. Bach, “Minimizing Finite Sums with the Stochastic Average Gradient,” may 2016, arXiv: 1309.2388v2 [math.OC]. [Online]. Available: https://hal.inria.fr/hal-00860051
2016
Closest in time.