4 papers
LMT: A Bayesian Framework for Causal Discovery from Textual Alarm Records in Manufacturing Systems
Xiaofeng Xiao, Jianhong Chen, Qiuzhuang Sun +2
Textual event records, such as alarm logs, have become an increasingly common data source in engineering and manufacturing systems. Beyond identifying correlations or recurring pat…
Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
Jianhong Chen, Naichen Shi, Xubo Yue
Causal discovery is a data-driven paradigm for analyzing complex systems, while physics-based models, such as ordinary differential equations (ODEs), provide mechanistic structure…
Toward Temporal Causal Representation Learning with Tensor Decomposition
Jianhong Chen, Meng Zhao, Mostafa Reisi Gahrooei +1
Temporal causal representation learning is a powerful tool for uncovering complex patterns in observational studies, which are often represented as low-dimensional time series. How…
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series Data
Jianhong Chen, Ying Ma, Xubo Yue
Traditionally, learning the structure of a Dynamic Bayesian Network has been centralized, requiring all data to be pooled in one location. However, in real-world scenarios, data ar…