1 citations · 2 across the 4 of their papers we have counts for
6 papers
Robustness Verification of Graph Neural Networks Via Lightweight Satisfiability Testing
Chia-Hsuan Lu, Tony Tan, Michael Benedikt
Graph neural networks (GNNs) are the predominant architecture for learning over graphs. As with any machine learning model, an important issue is the detection of attacks, where an…
Exact Verification of Graph Neural Networks with Incremental Constraint Solving
Minghao Liu, Chia-Hsuan Lu, Marta Kwiatkowska
Graph neural networks (GNNs) are increasingly often employed in high-stakes applications, such as fraud detection or healthcare, but are susceptible to adversarial attacks. A numbe…
Analysis of logics with arithmetic
Michael Benedikt, Chia-Hsuan Lu, Tony Tan
We present new results on finite satisfiability of logics with counting and arithmetic. One result is a tight bound on the complexity of satisfiability of logics with so-called loc…
Decidability of Graph Neural Networks via Logical Characterizations
Michael Benedikt, Chia-Hsuan Lu, Tony Tan
We present results concerning the expressiveness and decidability of a popular graph learning formalism, graph neural networks (GNNs), exploiting connections with logic. We use a f…
On two-variable guarded fragment logic with expressive local Presburger constraints
Chia-Hsuan Lu, Tony Tan
We consider the extension of the two-variable guarded fragment logic with local Presburger quantifiers. These are quantifiers that can express properties such as "the number of inc…
Towards a more efficient approach for the satisfiability of two-variable logic
Ting-Wei Lin, Chia-Hsuan Lu, Tony Tan
We revisit the satisfiability problem for two-variable logic, denoted by SAT(FO2), which is known to be NEXP-complete. The upper bound is usually derived from its well known Expone…