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Creators/Authors contains: "Burroni, Javier"

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  1. ; ; (, Association for Uncertainty in Artificial Intelligence)
    Accepted Manuscript Full Text Available
  2. ; ; (, The 40th Conference on Uncertainty in Artificial Intelligence)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, International Conference on Machine Learning)
    Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models. The advent of probabilistic programming languages (PPLs) frees users from writing inference algorithms and lets users focus on modeling. However, many models are difficult for HMC to solve directly, and often require tricks like model reparameterization. We are motivated by the fact that many of those models could be simplified by marginalization. We propose to use automatic marginalization as part of the sampling process using HMC in a graphical model extracted from a PPL, which substantially improves sampling from real-world hierarchical models. 
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    Accepted Manuscript Full Text Available