# Stochastic approximation

## Papers overview

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Highly Cited

2011

Highly Cited

2011

- NIPS
- 2011

In this paper, we consider the minimization of a convex objective function defined on a Hilbert space, which is only availableâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- SIAM Journal on Optimization
- 2008

In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basicâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- 2007

Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone functionâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- 2007

The Wangâ€“Landau (WL) algorithm is an adaptive Markov chain Monte Carlo algorithm used to calculate the spectral density for aâ€¦Â (More)

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Highly Cited

2005

Highly Cited

2005

- Proceedings of the 44th IEEE Conference onâ€¦
- 2005

In this paper we address the problem of the stability and convergence of the stochastic approximation procedure Î¸<inf>n+1</infâ€¦Â (More)

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Highly Cited

2000

Highly Cited

2000

- IEEE Trans. Automat. Contr.
- 2000

Stochastic approximation (SA) has long been applied for problems of minimizing loss functions or root finding with noisy inputâ€¦Â (More)

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Highly Cited

1998

Highly Cited

1998

- 1998

The convergence of online learning algorithms is analyzed using the tools of the stochastic approximation theory, and provedâ€¦Â (More)

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Highly Cited

1998

Highly Cited

1998

- 1998

The asymptotic behavior of a distributed, asynchronous stochastic approximation scheme is analyzed in terms of a limitingâ€¦Â (More)

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Highly Cited

1994

Highly Cited

1994

- Machine Learning
- 1994

We provide some general results on the convergence of a class of stochastic approximation algorithms and their parallel andâ€¦Â (More)

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Highly Cited

1987

Highly Cited

1987

- American Control Conference
- 1987

This paper shows how stochastic approximation (SA) can be used to construct maximum likelihood estimates of system parametersâ€¦Â (More)

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