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Found 7 papers, 2 papers with code
Sample Average Approximation for Black-Box VI
We present a novel approach for black-box VI that bypasses the difficulties of stochastic gradient ascent, including the task of selecting step-sizes.
U-Statistics for Importance-Weighted Variational Inference
We propose the use of U-statistics to reduce variance for gradient estimation in importance-weighted variational inference.
Automatically Marginalized MCMC in Probabilistic Programming
Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models.
The Random Conditional Distribution for Higher-Order Probabilistic Inference
The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas.
Soft Constraints for Inference with Declarative Knowledge
We develop a likelihood free inference procedure for conditioning a probabilistic model on a predicate.
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro.
Inference of Users Demographic Attributes based on Homophily in Communication Networks
Over the past decade, mobile phones have become prevalent in all parts of the world, across all demographic backgrounds.
