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Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which \textit{adapt} themselves to obtain better estimators over a sequence of iterations.
A stochastic approximation method
H. Robbins and S. Monro · 1951
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Population Monte Carlo
Olivier Cappé, Arnaud Guillin, Jean-Michel Marin, and Christian P Robert · 2004
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Monte Carlo statistical methods
Christian P Robert and George Casella · 2004
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Convergence of adaptive mixtures of importance sampling schemes
Randal Douc, Arnaud Guillin, J-M Marin, and Christian P Robert · 2007
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Adaptive importance sampling in general mixture classes
Olivier Cappé, Randal Douc, Arnaud Guillin, Jean-Michel Marin, and Christian P Robert · 2008
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Adaptive monte carlo variance reduction for lévy processes with two-time-scale stochastic approximation
Reiichiro Kawai · 2008
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael I Jordan · 2008
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A framework for adaptive monte carlo procedures
Bernard Lapeyre and Jérôme Lelong · 2011
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Introductory lectures on convex optimization: A basic course , volume 87
Yurii Nesterov · 2013
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
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Particle-kernel estimation of the filter density in state-space models
Dan Crisan and Joaquín Míguez · 2014
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Adaptive importance sampling via stochastic convex programming
Ernest K Ryu and Stephen P Boyd · 2014
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Convex optimization: Algorithms and complexity
Sébastien Bubeck et al · 2015
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Adaptive importance sampling in signal processing
Mónica F Bugallo, Luca Martino, and Jukka Corander · 2015
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Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2016
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Adaptive importance sampling for control and inference
Acceleration on adaptive importance sampling with sample average approximation
Reiichiro Kawai · 2017
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Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
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Minimizing finite sums with the stochastic average gradient
Mark Schmidt, Nicolas Le Roux, and Francis Bach · 2017
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Asymptotic bias of stochastic gradient search
Vladislav B Tadić and Arnaud Doucet · 2017
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The sample size required in importance sampling
Sourav Chatterjee, Persi Diaconis, et al · 2018
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Optimizing adaptive importance sampling by stochastic approximation
Reiichiro Kawai · 2018
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