4 papers
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…
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…