2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis
Jie Qiao, Yu Xiang, Zhengming Chen +2
Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientifi…
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples
Ruichu Cai, Yuxuan Zhu, Jie Qiao +3
Deep neural networks (DNNs) have been demonstrated to be vulnerable to well-crafted \emph{adversarial examples}, which are generated through either well-conceived -n…
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
Jie Qiao, Ruichu Cai, Siyu Wu +3
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes p…