Microsoft Research Cambridge

2013

Citations in the corpus

• Design and Implementation of Probabilistic Programming Language Anglican (2016, neutral): “…as Metropolis-Hastings (Wingate et al. 2011; Mansinghka et al. 2014; Yang et al. 2014), Hamiltonian Monte Carlo (Team), expectation propagation (Minka et al. 2010), and extensions of Sequential Monte Carlo (Wood et al. 2014; van de [Copyright notice will appear here once ’preprint’ option is…”
• Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages (2015, positive): “This is a goal sought 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].”
• Probabilistic programming in Python using PyMC3 (2016, neutral): “…low-level languages; notable examples include Church (Goodman et al., 2012) (derived from Scheme), Anglican (Wood et al., 2014) (integrated with Clojure and compiled with a Java Virtual Machine), Venture (Mansinghka et al., 2014) (built from C++), Infer.NET (Minka et al., 2010) (built upon the .”
• Automatic Differentiation Variational Inference (2016, neutral): “Winn and Bishop (2005); Minka et al. (2014) extend this to graphical models with non-conjugate relationships by either using custom approximations or an expensive sampling approach.”
• Inference Compilation and Universal Probabilistic Programming (2016, neutral): “Probabilistic programming systems such as STAN (Car-penter et al., 2015), BUGS (Lunn et al., 2000), and Infer.NET (Minka et al., 2014) allow efficient inference in a restricted space of generative models, while systems such as Church (Goodman et al., 2008), Venture (Mansinghka et al., 2014), and Angli”
• Black-Box Policy Search with Probabilistic Programs (2015, unknown): “Probabilistic programming systems [Milch et al., 2007, Goodman et al., 2008, Minka et al., 2014, Pfeffer, 2009, Mansinghka et al., 2014, Wood et al., 2014, Gordon et al., 2014] represent generative models as programs in a language that provides specialized syntax to instantiate random variables, as…”
• Non-conjugate Variational Message Passing for Multinomial and Binary Regression (2011, unknown): “This is a particular disadvantage for an inference framework such as Infer.NET [13] where we want to allow modular construction of models from arbitrary deterministic and stochastic factors.”
• Deep Probabilistic Programming (2017, unknown): “For example, Infer.NET enables fast message passing for graphical models (Minka et al., 2014), and Augur enables data parallelism with GPUs for Gibbs sampling in Bayesian networks (Tristan et al., 2014).”
• Variational inference in nonconjugate models (2012, unknown): “Knowles and Minka (2011) presented a message passing algorithm for non-conjugate models, which has been implemented in Infer.NET (Minka et al., 2010); their technique applies to a subset of models described in this paper.”