Probabilistic latent variable models

Latent variable models (LVMs) are a statistical tool to infer an unobserved, hidden state of a complex (e.g. biological) system based on observable data that is often high-dimensional. To this end, a high-dimensional dataset of correlated observations is reduced into a low-dimensional dataset of uncorrelated and interpretable latent variables. Probabilistic approaches allow for a principled way to disentangle distinct sources of variation and explicitly model dependencies between features as well as samples.

Accounting for dependencies between genes in LVMs

• Standard latent variable models only model dependencies between samples
• Can we make dependencies between features (genes) explicit?
• Use framework of Gaussian Process Latent Variable Models (GP-LVM)
• Probabilistic kernel PCA via GP regression with unobserved input
• Introduce kernel to model covariance between genes
• Learn latent variables for genes and samples and connect via Kronecker Product
• Apply to matrix completion tasks

Reference: Yang & Buettner, UAI 2021 (in revision)

Hierarchical autoencoders for Domain Generalisation

• Learn VAE to disentangle domain-specific information form class-specific information and residual variance
• Place Dirichlet prior on domain representation
• Learn “topics” that describe domain structure in unsupervised manner
• Interpretable model for unsupervised domain generalisation

Reference: Sun & Buettner, ICLR Workshop on robustML, 2021