3 papers
stat.ME2026
Beyond Local Independence: High-Dimensional Latent Class Graphical Models with Shared Block Structure
Seunghyun Lee, Yuqi Gu
Latent class models are central tools for multivariate categorical data from heterogeneous populations, but their standard local-independence assumption is often unrealistic in mod…
stat.ML2026
On Theoretical Identifiability of Discrete Latent Causal Graphical Models
Seunghyun Lee, Yuqi Gu
This paper considers a challenging problem of identifying a causal graphical model under the presence of latent variables. While various identifiability conditions have been propos…
stat.ML2025
Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
Seunghyun Lee, Yuqi Gu
In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statis…