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AND/OR Importance Sampling
The paper introduces AND/OR importance sampling for probabilistic graphi...
06/13/2012 ∙ by Vibhav Gogate, et al. ∙
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Efficient Black-Box Importance Sampling for VaR and CVaR Estimation
This paper considers Importance Sampling (IS) for the estimation of tail...
06/16/2021 ∙ by Anand Deo, et al. ∙
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Bayesian Update with Importance Sampling: Required Sample Size
Importance sampling is used to approximate Bayes' rule in many computati...
09/22/2020 ∙ by Daniel Sanz-Alonso, et al. ∙
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Sample Reuse via Importance Sampling in Information Geometric Optimization
In this paper we propose a technique to reduce the number of function ev...
05/31/2018 ∙ by Shinichi Shirakawa, et al. ∙
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Improving black-box optimization in VAE latent space using decoder uncertainty
Optimization in the latent space of variational autoencoders is a promis...
06/30/2021 ∙ by Pascal Notin, et al. ∙
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Sequential Importance Sampling With Corrections For Partially Observed States
We consider an evolving system for which a sequence of observations is b...
03/09/2021 ∙ by Valentina Di Marco, et al. ∙
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Learning to Importance Sample in Primary Sample Space
Importance sampling is one of the most widely used variance reduction st...
08/23/2018 ∙ by Quan Zheng, et al. ∙
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Black-box Importance Sampling
10/17/2016 ∙ by Qiang Liu, et al. ∙
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Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated. We address this problem by studying black-box importance sampling methods that calculate importance weights for samples generated from any unknown proposal or black-box mechanism. Our method allows us to use better and richer proposals to solve difficult problems, and (somewhat counter-intuitively) also has the additional benefit of improving the estimation accuracy beyond typical importance sampling. Both theoretical and empirical analyses are provided.
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