4 papers
ASIND: Alternating Sparse Identification for Predicting Network Dynamics Without Knowledge
Mingyu Kang, Jianxi Gao, Wenwu Yu +1
Identifying network dynamics is a critical yet challenging task to to understand the mechanism of real-world social systems. There are two types of algorithms, and one requires the…
Spatio-Temporal Graphical Counterfactuals: An Overview
Mingyu Kang, Duxin Chen, Ziyuan Pu +2
Counterfactual thinking is a crucial yet challenging topic for artificial intelligence to learn knowledge from data and ultimately improve performance for new scenarios. Many resea…
The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
Huiyang Yi, Yanyan He, Duxin Chen +3
Causal discovery aims to learn causal relationships between variables from targeted data, making it a fundamental task in machine learning. However, causal discovery algorithms oft…
Identifying Unique Spatial-Temporal Bayesian Network without Markov Equivalence
Mingyu Kang, Duxin Chen, Ning Meng +2
Identifying vanilla Bayesian network to model spatial-temporal causality can be a critical yet challenging task. Different Markovian-equivalent directed acyclic graphs would be ide…