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Found 5 papers, 2 papers with code
Automatically Marginalized MCMC in Probabilistic Programming
Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models.
Variational Marginal Particle Filters
We reveal that the marginal particle filter is obtained from sequential Monte Carlo by applying Rao-Blackwellization operations, which sacrifices the trajectory information for reduced variance and differentiability.
Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies
Off-policy evaluation is a key component of reinforcement learning which evaluates a target policy with offline data collected from behavior policies.
Shallow VAEs with RealNVP Prior Can Perform as Well as Deep Hierarchical VAEs
Using powerful posterior distributions is a popular technique in variational inference.
On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder
Using powerful posterior distributions is a popular approach to achieving better variational inference.
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