Fetching the paper…
Reading the bibliography…
The need for parameter estimation with massive datasets has reinvigorated interest in stochastic optimization and iterative estimation procedures.
The importance of better models in stochastic optimization
Asi, H. and J. C. Duchi (2019) · 1903
Earlier work this paper cites.
Stochastic proximal splitting algorithm for stochastic composite minimization
Patrascu, A. and P. Irofti (2019) · 1912
Earlier work this paper cites.
A stochastic approximation method
Robbins, H. and S. Monro (1951) · 1951
Earlier work this paper cites.
Approximation methods which converge with probability one
Blum, J. R. (1954, 06) · 1954
Earlier work this paper cites.
Asymptotic distribution of stochastic approximation procedures
Sacks, J. (1958) · 1958
Earlier work this paper cites.
Adaptive switching circuits
Widrow, B. and M. E. Hoff (1960) · 1960
Earlier work this paper cites.
On stochastic approximation
Gladyshev, E. (1965) · 1965
Earlier work this paper cites.
A learning method for system identification
Nagumo, J.-I. and A. Noda (1967) · 1967
Earlier work this paper cites.
On asymptotic normality in stochastic approximation
Fabian, V. (1968) · 1968
Earlier work this paper cites.
Stochastic signal representation
Coraluppi, G. and T. Y. Young (1969) · 1969
Earlier work this paper cites.
Stochastic approximation and recursive estimation
Nevel’son, M. B., R. Z. Khas’minskii, and B. Silver (1973) · 1973
Earlier work this paper cites.
Monotone operators and the proximal point algorithm
Rockafellar, R. T. (1976) · 1976
Earlier work this paper cites.
A convergence theorem for non negative almost supermartingales and some applications
Robbins, H. and D. Siegmund (1985) · 1985
Earlier work this paper cites.
Multivariate adaptive stochastic approximation
Wei, C. (1987) · 1987
Earlier work this paper cites.
Efficient estimations from a slowly convergent robbins-monro process
Ruppert, D. (1988) · 1988
Earlier work this paper cites.
Adaptive algorithms and stochastic approximations
Benveniste, A., P. Priouret, and M. Métivier (1990) · 1990
Earlier work this paper cites.
Stochastic approximation and optimization of random systems
Ljung, L., G. Pflug, and H. Walk (1992) · 1992
Earlier work this paper cites.
On likelihood and bayesian methods for interval estimation of the ld50
Grieve, A. P. (1996) · 1996
Earlier work this paper cites.
Natural gradient works efficiently in learning
Amari, S.-I. (1998) · 1998
Cited alongside, same era.
Stochastic approximation and recursive algorithms and applications
Kushner, H. J. and G. Yin (2003) · 2003
Cited alongside, same era.
Stochastic approximation
Lai, T. L. et al. (2003) · 2003
Cited alongside, same era.
Introductory lectures on convex optimization
Nesterov, Y. (2004) · 2004
Cited alongside, same era.
Solving large scale linear prediction problems using stochastic gradient descent algorithms
Zhang, T. (2004) · 2004
Cited alongside, same era.
Stochastic approximation
Borkar, V. S. (2008) · 2008
Cited alongside, same era.
Robust stochastic approximation approach to stochastic programming
Stochastic proximal iteration: a non-asymptotic improvement upon stochastic gradient descent
Ryu, E. K. and S. Boyd (2014) · 2014
Later among the works it cites.
Statistical analysis of stochastic gradient methods for generalized linear models
Toulis, P., E. Airoldi, and J. Rennie (2014) · 2014
Later among the works it cites.
A universal catalyst for first-order optimization
Lin, H., J. Mairal, and Z. Harchaoui (2015) · 2015
Closest in time.
Scalable estimation strategies based on stochastic approximations: classical results and new insights
Toulis, P. and E. M. Airoldi (2015) · 2015
Closest in time.
Ergodic convergence of a stochastic proximal point algorithm
Bianchi, P. (2016) · 2016
Closest in time.
Dynamical behavior of a stochastic forward–backward algorithm using random monotone operators
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nemirovski, A., A. Juditsky, G. Lan, and A. Shapiro (2009) · 2009
Cited alongside, same era.
Efficient learning using forward-backward splitting
Singer, Y. and J. C. Duchi (2009) · 2009
Cited alongside, same era.
Large-scale machine learning with stochastic gradient descent
Bottou, L. (2010) · 2010
Cited alongside, same era.
Implicit online learning
Kulis, B. and P. L. Bartlett (2010) · 2010
Cited alongside, same era.
Numerical analysis for statisticians
Lange, K. (2010) · 2010
Cited alongside, same era.
Convex analysis and monotone operator theory in Hilbert spaces
Bauschke, H. H. and P. L. Combettes (2011) · 2011
Cited alongside, same era.
Bianchi, P. and W. Hachem (2016) · 2016
Closest in time.
Optimization methods for large-scale machine learning
Bottou, L., F. E. Curtis, and J. Nocedal (2016) · 2016
Closest in time.
Statistical inference for model parameters in stochastic gradient descent
Chen, X., J. D. Lee, X. T. Tong, and Y. Zhang (2016) · 2016
Closest in time.
A stochastic inertial forward–backward splitting algorithm for multivariate monotone inclusions
Rosasco, L., S. Villa, and B. C. Vũ (2016) · 2016
Closest in time.
Towards stability and optimality in stochastic gradient descent
Tran, D., P. Toulis, and E. Airoldi (2016) · 2016
Closest in time.
Statistical inference using sgd
Li, T., L. Liu, A. Kyrillidis, and C. Caramanis (2017) · 2017
Closest in time.
Patrascu, A. and I. Necoara (2017) · 2017
Closest in time.
Asymptotic and finite-sample properties of estimators based on stochastic gradients
Toulis, P. and E. M. Airoldi (2017, 08) · 2017
Closest in time.
A constant step Forward-Backward algorithm involving random maximal monotone operators
Bianchi, P., W. Hachem, and A. Salim (2018) · 2018
Closest in time.
Robust implicit backpropagation
Fagan, F. and G. Iyengar (2018) · 2018
Closest in time.
Su, W. and Y. Zhu (2018) · 2018
Closest in time.
Snake: a stochastic proximal gradient algorithm for regularized problems over large graphs
Salim, A., P. Bianchi, and W. Hachem (2019) · 2019
Closest in time.