Variational Inference for Diffusion Processes

By Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor. 2007

106 citations recorded by the source corpus.

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, background): “…Dunson 2011; Armagan and Dunson 2011; Neville, Ormerod, and Wand 2014), general time-series models (Roberts et al. 2004; Barber and Chiappa 2006; Archambeau et al. 2007a, 2007b; Johnson and Willsky 2014; Foti et al. 2014), robust models (Tipping and Lawrence 2005; Wang and Blei 2015), and…”
Scalable Gradients for Stochastic Differential Equations (2020, background): “The special case with constant diffusion coefficients was considered more than a decade ago [5].”
Optimal control theory and the linear bellman equation (2011, methodology): “One can also use a variational approximation to approximate the path integral using the variational approach for diffusion processes Archambeau et al. (2008), or use the EP approximation Mensink et al. (2010). An illustration of the variational approximation to a particular simple n joint arm is pre”
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit (2019, methodology): “Generative models of this type have been used considered in earlier work, ﬁrst by Movellan et al. [2002] as a noisy continuous-time counterpart of recurrent neural nets, and, more recently, by Archambeau et al. [2007], Hashimoto et al. [2016], and Ryder et al. [2018].”
Graphical Model Inference in Optimal Control of Stochastic Multi-Agent Systems (2008, methodology): “The variational approximation approximates the path integral (11) by a Gaussian process ( Archambeau, Opper, Shen, Cornford, & Shawe-Taylor, 2007 ), and could be particularly useful in the high noise regime.”
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes (2010, background): “…(Lappalainen and Honkela, 2000; Honkela and Valpola, 2005; Honkela et al., 2007) and nonlinear dynamical models (Valpola and Karhunen, 2002; Archambeau et al., 2008), non-conjugate variance models (Valpola et al., 2004; Raiko et al., 2007) as well as Gaussian process latent variable models…”
Continuous Time Dynamic Topic Models (2008, methodology): “For continuous time processes, variational inference has been applied in Markov jump processes [1] and diffusion processes [17], where the variational distributions are also random processes.”
Black-Box Variational Inference for Stochastic Differential Equations (2018, background): “The code is available at https://github.com/Tom-Ryder/VIforSDEs .”