9 citations · 14 across the 3 of their papers we have counts for
3 papers
cs.AI2022★ 1 cited
On Deciding Feature Membership in Explanations of SDD & Related Classifiers
Xuanxiang Huang, Joao Marques-Silva
When reasoning about explanations of Machine Learning (ML) classifiers, a pertinent query is to decide whether some sensitive features can serve for explaining a given prediction.…
cs.AI2021★ 9 cited
Efficient Explanations for Knowledge Compilation Languages
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev +3
Knowledge compilation (KC) languages find a growing number of practical uses, including in Constraint Programming (CP) and in Machine Learning (ML). In most applications, one natur…
cs.AI2021★ 4 cited
On Efficiently Explaining Graph-Based Classifiers
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev +1
Recent work has shown that not only decision trees (DTs) may not be interpretable but also proposed a polynomial-time algorithm for computing one PI-explanation of a DT. This paper…