Copula variational inference

By Dustin Tran, David M. Blei, Edoardo M. Airoldi. 2015

108 citations recorded by the source corpus.

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Citations in the corpus

The Variational Gaussian Process (2015, background): “Many expressive variational approximations can be viewed under this construct (Saul & Jordan, 1996; Jaakkola & Jordan, 1998; Rezende & Mohamed, 2015; Tran et al., 2015).”
Hierarchical Variational Models (2015, methodology): “(Titsias and Lázaro-Gredilla, 2014) COPULA VI (Tran et al., 2015) O ( d by making both the parameters of the variational model and recursive posterior approximation functions of their conditioning sets.”
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm (2016, background): “Several other works also leverage variable transforms in variational inference, but with more limited forms; examples include affine transforms [19, 20], and recently the copula models that correspond to element-wise transforms over the individual variables [21, 22].”
Deep and Hierarchical Implicit Models (2017, background): “Recent advances in variational inference have developed expressive variational approximations (Rezende & Mo-hamed, 2015; Salimans et al., 2015; Tran et al., 2015).”
Advances in Variational Inference (2017, unknown): “latent variables is copula VI [60], [199].”
Importance Weighted Hierarchical Variational Inference (2019, background): “One possible approach is to augment some standard q(z|x) with help of copulas (Tran et al., 2015), mixtures (Guo et al., 2016; Gershman et al., 2012), or invertible transformations with tractable Jacobians also known as normalizing flows (Rezende and Mohamed, 2015; Kingma et al., 2016; Dinh et al.,…”
Semi-Implicit Variational Inference (2018, background): “…back to Q to partially restore the dependencies (Saul & Jordan, 1996; Jaakkola & Jordan, 1998; Hoffman & Blei, 2015; Giordano et al., 2015; Tran et al., 2015; 2016; Han et al., 2016; Ranganath et al., 2016; Maaløe et al., 2016; Gregor et al., 2015), it is still necessary for Q to have an…”
AI in Finance: Challenges, Techniques, and Opportunities (2021, unknown): “, copula-based high-dimensional dependence modeling and ARIMA/GARCH-based timeseries regression) [153] for market movement forecasting, portfolio investment prediction, and risk analysis.”
Hierarchical Implicit Models and Likelihood-Free Variational Inference (2017, background): “The final thread is variational inference with expressive approximations [45, 48, 52].”
Doubly Semi-Implicit Variational Inference (2018, background): “They can be divided roughly into two main groups: those that require the probability density function of the approximate posterior to be analytically tractable (which we will call explicit models) [30, 12, 9, 5, 36, 38, 6, 27, 20] and those that do not (implicit models) [11, 22, 37, 17, 21, 31, 40].”