21 citations · 46 across the 5 of their papers we have counts for
9 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…
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…
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
Joao Marques-Silva, Thomas Gerspacher, Martin C. Cooper +2
Recent work proposed the computation of so-called PI-explanations of Naive Bayes Classifiers (NBCs). PI-explanations are subset-minimal sets of feature-value pairs that are suffici…
Strengthening neighbourhood substitution
Martin C. Cooper
Domain reduction is an essential tool for solving the constraint satisfaction problem (CSP). In the binary CSP, neighbourhood substitution consists in eliminating a value if there…
Steepest ascent can be exponential in bounded treewidth problems
David A. Cohen, Martin C. Cooper, Artem Kaznatcheev +1
We investigate the complexity of local search based on steepest ascent. We show that even when all variables have domains of size two and the underlying constraint graph of variabl…