Search Results for author:
Found 7 papers, 3 papers with code
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
We show that if these auxiliary densities are constructed such that they overlap with $p$ and $q$, then a multi-class logistic regression allows for estimating $\log p/q$ on the domain of any of the $K+2$ distributions and resolves the distribution shift problems of the current state-of-the-art methods.
Enhanced gradient-based MCMC in discrete spaces
The recent introduction of gradient-based MCMC for discrete spaces holds great promise, and comes with the tantalising possibility of new discrete counterparts to celebrated continuous methods such as MALA and HMC.
Statistical applications of contrastive learning
The likelihood function plays a crucial role in statistical inference and experimental design.
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
We address this gap by introducing variational Gibbs inference (VGI), a new general-purpose method to estimate the parameters of statistical models from incomplete data.
Scaling Densities For Improved Density Ratio Estimation
As such, estimating density ratios accurately using only samples from $p$ and $q$ is of high significance and has led to a flurry of recent work in this direction.
Telescoping Density-Ratio Estimation
Density-ratio estimation via classification is a cornerstone of unsupervised learning.
Variational Noise-Contrastive Estimation
The core idea is to use a variational lower bound to the NCE objective function, which can be optimised in the same fashion as the evidence lower bound (ELBO) in standard variational inference (VI).
