9 citations · 27 across the 5 of their papers we have counts for
6 papers
Provably Precise, Succinct and Efficient Explanations for Decision Trees
Yacine Izza, Alexey Ignatiev, Nina Narodytska +2
Decision trees (DTs) embody interpretable classifiers. DTs have been advocated for deployment in high-risk applications, but also for explaining other complex classifiers. Neverthe…
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…
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…
Efficient Explanations With Relevant Sets
Yacine Izza, Alexey Ignatiev, Nina Narodytska +2
Recent work proposed -relevant inputs (or sets) as a probabilistic explanation for the predictions made by a classifier on a given input. -relevant sets are significant becau…
On Explaining Random Forests with SAT
Yacine Izza, Joao Marques-Silva
Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers. Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for com…
On Explaining Decision Trees
Yacine Izza, Alexey Ignatiev, Joao Marques-Silva
Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models. This is informally motivated by paths in DTs being often much smaller tha…