Density Ratio Estimation

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given …

We reformulate the computation of the acquisition function in Bayesian optimization (BO) as a probabilistic classification problem, providing advantages in scalability, flexibility, and representational capacity, while casting aside the limitations of tractability constraints on the model.

This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.

We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent …