2 citations · 6 across the 12 of their papers we have counts for
14 papers
Towards Identifiable Representations under Misspecified Structure
Yuke Li, Yujia Zheng, Ziyi Chen +2
The presence of noise that depends on the latent variables poses a fundamental challenge to identifiability. Existing results rely on conditional independence among the observation…
Nonparametric Identification of Latent Concepts
Yujia Zheng, Shaoan Xie, Kun Zhang
We are born with the ability to learn concepts by comparing diverse observations. This helps us to understand the new world in a compositional manner and facilitates extrapolation,…
LLM Interpretability with Identifiable Temporal-Instantaneous Representation
Xiangchen Song, Jiaqi Sun, Zijian Li +2
Despite Large Language Models' remarkable capabilities, understanding their internal representations remains challenging. Mechanistic interpretability tools such as sparse autoenco…
Causal evidence for the primordiality of colours in trans-Neptunian objects
Benjamin L. Davis, Mohamad Ali-Dib, Yujia Zheng +3
The origins of the colours of Trans-Neptunian Objects (TNOs) represent a crucial unresolved question, central to understanding the history of our Solar System. Recent observational…
Causal Evidence for the Primordiality of Colors in Trans-Neptunian Objects
Benjamin L. Davis, Mohamad Ali-Dib, Yujia Zheng +3
The origins of the colors of Trans-Neptunian Objects (TNOs) represent a crucial unresolved question, central to understanding the history of our Solar System. Recent observational…
Nonparametric Factor Analysis and Beyond
Yujia Zheng, Yang Liu, Jiaxiong Yao +2
Nearly all identifiability results in unsupervised representation learning inspired by, e.g., independent component analysis, factor analysis, and causal representation learning, r…
Type Information-Assisted Self-Supervised Knowledge Graph Denoising
Jiaqi Sun, Yujia Zheng, Xinshuai Dong +2
Knowledge graphs serve as critical resources supporting intelligent systems, but they can be noisy due to imperfect automatic generation processes. Existing approaches to noise det…
Identifying Selections for Unsupervised Subtask Discovery
Yiwen Qiu, Yujia Zheng, Kun Zhang
When solving long-horizon tasks, it is intriguing to decompose the high-level task into subtasks. Decomposing experiences into reusable subtasks can improve data efficiency, accele…
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
Ignavier Ng, Yujia Zheng, Xinshuai Dong +1
Independent component analysis (ICA) is a fundamental statistical tool used to reveal hidden generative processes from observed data. However, traditional ICA approaches struggle w…
Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight
Zhiqiang Xie, Yujia Zheng, Lizi Ottens +3
Runtime failure and performance degradation is commonplace in modern cloud systems. For cloud providers, automatically determining the root cause of incidents is paramount to ensur…
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
Haoyue Dai, Ignavier Ng, Gongxu Luo +3
Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros represe…
Local Causal Discovery with Linear non-Gaussian Cyclic Models
Haoyue Dai, Ignavier Ng, Yujia Zheng +2
Local causal discovery is of great practical significance, as there are often situations where the discovery of the global causal structure is unnecessary, and the interest lies so…
Causal Representation Learning from Multiple Distributions: A General Setting
Kun Zhang, Shaoan Xie, Ignavier Ng +1
In many problems, the measured variables (e.g., image pixels) are just mathematical functions of the latent causal variables (e.g., the underlying concepts or objects). For the pur…
Generalizing Nonlinear ICA Beyond Structural Sparsity
Yujia Zheng, Kun Zhang
Nonlinear independent component analysis (ICA) aims to uncover the true latent sources from their observable nonlinear mixtures. Despite its significance, the identifiability of no…