Search Results for author:
Found 2 papers, 1 papers with code
Prediction under Latent Subgroup Shifts with High-Dimensional Observations
We introduce a new approach to prediction in graphical models with latent-shift adaptation, i. e., where source and target environments differ in the distribution of an unobserved confounding latent variable.
Unsupervised representation learning with recognition-parametrised probabilistic models
We introduce a new approach to probabilistic unsupervised learning based on the recognition-parametrised model (RPM): a normalised semi-parametric hypothesis class for joint distributions over observed and latent variables.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
