Fetching the paper…
Reading the bibliography…
We consider the minimization of an objective function given access to unbiased estimates of its gradient through stochastic gradient descent (SGD) with constant step-size.
A stochastic approxiation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
Analysis of recursive stochastic algorithms
L. Ljung · 1977
Earlier work this paper cites.
Stochastic approximation methods for constrained and unconstrained systems
H. J. Kushner and D. S. Clark · 1978
Earlier work this paper cites.
Ordinary Differential Equations: Second Edition
P. Hartman · 1982
Earlier work this paper cites.
Problem complexity and method efficiency in optimization
A. S. Nemirovsky and D. B. Yudin · 1983
Earlier work this paper cites.
Applications of a Kushner and Clark lemma to general classes of stochastic algorithms
M. Métivier and P. Priouret · 1984
Earlier work this paper cites.
Stochastic minimization with constant step-size: asymptotic laws
G. Pflug · 1986
Earlier work this paper cites.
Théorèmes de convergence presque sure pour une classe d’algorithmes stochastiques à pas décroissant
M. Métivier and P. Priouret · 1987
Earlier work this paper cites.
Efficient estimations from a slowly convergent Robbins-Monro process
D. Ruppert · 1988
Earlier work this paper cites.
Adaptive algorithms and stochastic approximations
A. Benveniste, M. Métivier, and P. Priouret · 1990
Earlier work this paper cites.
Expansion of the global error for numerical schemes solving stochastic differential equations
D. Talay and L. Tubaro · 1990
Earlier work this paper cites.
Stochastic approximation and optimization of random systems
L. Ljung, G. Pflug, and H. Walk · 1992
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
B. T. Polyak and A. B. Juditsky · 1992
Earlier work this paper cites.
Nonlinear programming
D. Bertsekas · 1995
Earlier work this paper cites.
A dynamical system approach to stochastic approximations
M. Benaim · 1996
Earlier work this paper cites.
Convergence of stochastic algorithms: from the Kushner-Clark theorem to the Lyapounov functional method
J.-C. Fort and G. Pagès · 1996
Earlier work this paper cites.
A Liapunov bound for solutions of the Poisson equation
P. Glynn and S. Meyn · 1996
Earlier work this paper cites.
D. L. Zhu and P. Marcotte · 1996
Earlier work this paper cites.
About the multidimensional competitive learning vector quantization algorithm with constant gain
C. Bouton and G. Pagès · 1997
Earlier work this paper cites.
Random Perturbations of Dynamical Systems
M. I. Freidlin and A. D. Wentzell · 1998
Earlier work this paper cites.
A remark on the stability of the l.m.s. tracking algorithm
P. Priouret and A. Veretenikov · 1998
Cited alongside, same era.
Asymptotic behavior of a Markovian stochastic algorithm with constant step
J.-C. Fort and G. Pagès · 1999
Cited alongside, same era.
On a perturbation approach for the analysis of stochastic tracking algorithms
R. Aguech, E. Moulines, and P. Priouret · 2000
Cited alongside, same era.
Convergence rate of incremental subgradient algorithms
A. Nedić and D. Bertsekas · 2001
Cited alongside, same era.
Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise
J. C. Mattingly, A. M. Stuart, and D. J. Higham · 2002
Cited alongside, same era.
Introductory Lectures on Convex Optimization: A Basic Course
Y. Nesterov · 2004
Cited alongside, same era.
Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
O. Shamir and T. Zhang · 2013
Later among the works it cites.
Introduction to numerical analysis
J. Stoer and R. Bulirsch · 2013
Later among the works it cites.
High order numerical approximation of the invariant measure of ergodic SDEs
A. Abdulle, G. Vilmart, and K. C. Zygalakis · 2014
Later among the works it cites.
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
F. Bach · 2014
Later among the works it cites.
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
D. Needell, R. Ward, and N. Srebro · 2014
Later among the works it cites.
On the convergence of stochastic gradient MCMC algorithms with high-order integrators
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On recursive estimation for time varying autoregressive processes
E. Moulines, P. Priouret, and F. Roueff · 2005
Cited alongside, same era.
Why do partitions occur in Faa di Bruno’s chain rule for higher derivatives?
E. Levy · 2006
Cited alongside, same era.
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
S. Shalev-Shwartz, Y. Singer, and N. Srebro · 2007
Cited alongside, same era.
The tradeoffs of large scale learning
L. Bottou and O. Bousquet · 2008
Cited alongside, same era.
Confidence level solutions for stochastic programming
Y. Nesterov and J. Ph. Vial · 2008
Cited alongside, same era.
Markov Chains and Stochastic Stability
S. Meyn and R. Tweedie · 2009
Cited alongside, same era.
C. Chen, N. Ding, and L. Carin · 2015
Later among the works it cites.
Averaged least-mean-squares: bias-variance trade-offs and optimal sampling distributions
A. Défossez and F. Bach · 2015
Later among the works it cites.
Nonparametric stochastic approximation with large step-sizes
A. Dieuleveut and F. Bach · 2016
Later among the works it cites.
Harder, better, faster, stronger convergence rates for least-squares regression
A. Dieuleveut, N. Flammarion, and F. Bach · 2016
Later among the works it cites.
Stochastic gradient Richardson-Romberg Markov Chain Monte Carlo
A. Durmus, U. Şimşekli, E. Moulines, R. Badeau, and G. Richard · 2016
Later among the works it cites.
Parallelizing stochastic approximation through mini-batching and tail-averaging
P. Jain, S. M. Kakade, R. Kidambi, P. Netrapalli, and A. Sidford · 2016
Later among the works it cites.
A variational analysis of stochastic gradient algorithms
S. Mandt, M. Hoffman, and D. M. Blei · 2016
Later among the works it cites.
Convergence diagnostics for stochastic gradient descent with constant step size
J. Chee and P. Toulis · 2017
Closest in time.
Theoretical guarantees for approximate sampling from smooth and log-concave densities
A. S. Dalalyan · 2017
Closest in time.
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
A. Durmus and É. Moulines · 2017
Closest in time.
Accelerating Stochastic Gradient Descent
P. Jain, S. M. Kakade, R. Kidambi, P. Netrapalli, and A. Sidford · 2017
Closest in time.
Stochastic gradient descent as approximate Bayesian inference
S. Mandt, M. D Hoffman, and D. M. Blei · 2017
Closest in time.
Asymptotic bias of stochastic gradient search
V. B. Tadić and A. Doucet · 2017
Closest in time.
Supplement to bridging the gap between constant step size stochastic gradient descent and Markov chains, 2018
A. Dieuleveut, A. Durmus, and Bach F · 2018
Closest in time.