6 papers
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…
Discrete Causal Representation Learning
Wenjin Zhang, Yixin Wang, Yuqi Gu
Causal representation learning seeks to uncover causal relationships among high-level latent variables from low-level, entangled, and noisy observations. Existing approaches often…
Scalable Text-Embedding-informed Cognitive Diagnosis of Large Language Models
Jia Liu, Zhiyu Xu, Yuqi Gu
Large language models (LLMs) have achieved remarkable performance on diverse benchmarks, yet existing evaluation practices largely rely on coarse summary metrics that obscure under…
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…
Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length
Zhiyu Xu, Jia Liu, Yixin Wang +1
The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications and actionable future improvements. The…
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…