Nonstandard Interpretations of Probabilistic Programs for Efficient Inference

By David Wingate, Noah D. Goodman, Andreas Stuhlmüller, Jeffrey Mark Siskind. 2011

36 citations recorded by the source corpus.

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Citations in the corpus

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages (2015, background): “This is a goal ∗Currently at Google DeepMind. sought, for instance, in probabilistic programming languages (Wingate et al., 2011; Goodman et al., 2008), as well as in more granular, component-based systems (Stan Development Team, 2014; Minka et al., 2014).”
Unsupervised Learning of 3D Structure from Images (2016, background): “Traditional approaches to vision as inverse graphics [24, 21, 23] and analysis-by-synthesis [28, 34, 19, 35] rely on heavily engineered visual features with which inference of object properties such as shape and pose is substantially simpliﬁed.”
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs (2013, background): “Generative models for a range of image parsing tasks are also being explored [17, 4, 18, 22, 20].”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, methodology): “For “lightweight” implementations based on direct transformational compilation or augmented interpretation, such as Bher (Wingate et al., 2011b), this is a basic consequence of the approach: the entire program must be re-simulated to make a change to a single latent variable.”
Learning to Pass Expectation Propagation Messages (2013, background): “For example, [15] attempts to infer basic interaction structure from general probabilistic program specifications.”
Monte Carlo Gradient Estimation in Machine Learning (2019, methodology): “Many of the approaches for automated inference in probabilistic programs, like variational inference, rely on the automated computation of Monte Carlo gradient estimates (Kucukelbir et al., 2017; Ritchie et al., 2016; Wingate et al., 2011).”
Probabilistic programming (2014, result): “In contrast, previous approaches [21,49,58,59] have not specifically addressed rejections due to failing observations.”
R2: An Efficient MCMC Sampler for Probabilistic Programs (2014, methodology): “Wingate et al. (Wingate et al. 2011) use nonstandard interpretations during runtime execution to compute derivatives, track provenance, and use these computations to improve the efficiency of MCMC sampling.”
Deep Generative Vision as Approximate Bayesian Computation (2014, background): “Moreover, generative models for natural images have been widely explored by a variety of analysis-by-synthesis approaches [19, 5, 24, 22].”