5 citations · 6 across the 5 of their papers we have counts for
5 papers
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
Akash Srivastava, Seungwook Han, Kai Xu +2
Functions of the ratio of the densities are widely used in machine learning to quantify the discrepancy between the two distributions and . For high-dimensional distri…
Enhanced gradient-based MCMC in discrete spaces
Benjamin Rhodes, Michael Gutmann
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 contin…
Statistical applications of contrastive learning
Michael U. Gutmann, Steven Kleinegesse, Benjamin Rhodes
The likelihood function plays a crucial role in statistical inference and experimental design. However, it is computationally intractable for several important classes of statistic…
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann
Statistical models are central to machine learning with broad applicability across a range of downstream tasks. The models are controlled by free parameters that are typically esti…
Telescoping Density-Ratio Estimation
Benjamin Rhodes, Kai Xu, Michael U. Gutmann
Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and ge…