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Zefeng Liang

2 accepted papers

2024

On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function

NeurIPS 2024spotlight

Causal discovery from observational data, especially for count data, is essential across scientific and industrial contexts, such as biology, economics, and network operation maintenance. For this task, most approaches model count data using Bayesian networks or ordinal relations. However, they over…

Cited by 0SourcePDFScholar
2024

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples

AAAI 2024technical

Deep neural networks (DNNs) have been demonstrated to be vulnerable to well-crafted adversarial examples, which are generated through either well-conceived L_p-norm restricted or unrestricted attacks. Nevertheless, the majority of those approaches assume that adversaries can modify any features as t…