1 citations · 3 across the 11 of their papers we have counts for
13 papers
Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models
Zijian Li, Ruichu Cai, Feng Xie +7
Uncovering latent variables and their causal relations from observed data is a fundamental yet challenging problem. Existing methods often rely on restrictive assumptions, such as…
Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Zheng Li, Hao Zhang, Ruxin Wang +3
Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene…
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
Zijian Li, Minghao Fu, Junxian Huang +5
Modeling hierarchical latent dynamics behind time series data is critical for capturing temporal dependencies across multiple levels of abstraction in real-world tasks. However, ex…
Online Time Series Forecasting with Theoretical Guarantees
Zijian Li, Changze Zhou, Minghao Fu +6
This paper is concerned with online time series forecasting, where unknown distribution shifts occur over time, i.e., latent variables influence the mapping from historical to futu…
Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants
Wei Chen, Wanyang Gu, Linjun Peng +3
Federated causal discovery aims to uncover the causal relationships between entities while protecting data privacy, which has significant importance and numerous applications in re…
Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning
Zijian Li, Shunxing Fan, Yujia Zheng +6
Disentangled representation learning aims to uncover latent variables underlying the observed data, and generally speaking, rather strong assumptions are needed to ensure identifia…
Causal Temporal Representation Learning with Nonstationary Sparse Transition
Xiangchen Song, Zijian Li, Guangyi Chen +4
Causal Temporal Representation Learning (Ctrl) methods aim to identify the temporal causal dynamics of complex nonstationary temporal sequences. Despite the success of existing Ctr…
Learning Discrete Latent Variable Structures with Tensor Rank Conditions
Zhengming Chen, Ruichu Cai, Feng Xie +5
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Mos…
From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals
Ruichu Cai, Zhifang Jiang, Zijian Li +6
Existing methods for multi-modal time series representation learning aim to disentangle the modality-shared and modality-specific latent variables. Although achieving notable perfo…
On the Identification of Temporally Causal Representation with Instantaneous Dependence
Zijian Li, Yifan Shen, Kaitao Zheng +5
Temporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal p…
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
Zijian Li, Ruichu Cai, Zhenhui Yang +6
As environments evolve, temporal distribution shifts can degrade time series forecasting performance. A straightforward solution is to adapt to nonstationary changes while preservi…
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants
Wei Chen, Zhiyi Huang, Ruichu Cai +2
Causal discovery with latent variables is a crucial but challenging task. Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identifie…
Identifying Semantic Component for Robust Molecular Property Prediction
Zijian Li, Zunhong Xu, Ruichu Cai +5
Although graph neural networks have achieved great success in the task of molecular property prediction in recent years, their generalization ability under out-of-distribution (OOD…