Results 21 to 30 of about 11,193 (260)
CoRR, 2021 We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting. NVI is applicable to many commonly-used importance sampling strategies and provides a mechanism for learning intermediate densities, which can serve as ...
Esmaeili, B. +3 more
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An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment
Mathematics, 2023 In practical applications, learning models that can perform well even when the data distribution is different from the training set are essential and meaningful. Such problems are often referred to as out-of-distribution (OOD) generalization problems. In
Hang Su, Wei Wang
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An Introduction to Variational Inference
CoRR, 2021 13 pages, 9 ...
Ankush Ganguly, Samuel W. F. Earp
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Nonparametric variational inference [PDF]
CoRR, 2012 ICML2012
Samuel Gershman +2 more
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Stochastic Variational Inference
J. Mach. Learn. Res., 2012 We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference,
Matthew D. Hoffman +3 more
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Variational Inference for Logical Inference
CoRR, 2017 Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be interpreted in terms of truth. Here we make two contributions to this framework. The first is to show how a type of logical inference can be performed by evaluating conditional probabilities.
Emerson, Guy, Copestake, Ann
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Wasserstein Variational Inference
CoRR, 2018 This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasserstein distance as special cases.
Ambrogioni, L. +5 more
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Memorized Variational Continual Learning for Dirichlet Process Mixtures
IEEE Access, 2019 Bayesian nonparametric models are theoretically suitable for streaming data due to their ability to adapt model complexity with the observed data. However, very limited work has addressed posterior inference in a streaming fashion, and most of the ...
Yang Yang, Bo Chen, Hongwei Liu
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Implicit Copula Variational Inference
Journal of Computational and Graphical Statistics, 2022 Key to effective generic, or "black-box", variational inference is the selection of an approximation to the target density that balances accuracy and speed. Copula models are promising options, but calibration of the approximation can be slow for some choices. Smith et al.
Michael Stanley Smith, Ruben Loaiza-Maya
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Operator Variational Inference
CoRR, 2016 Appears in Neural Information Processing Systems ...
Rajesh Ranganath +3 more
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