47 citations · 179 across the 22 of their papers we have counts for
6 papers · 1 filter
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…
Explanations for Monotonic Classifiers
Joao Marques-Silva, Thomas Gerspacher, Martin Cooper +2
In many classification tasks there is a requirement of monotonicity. Concretely, if all else remains constant, increasing (resp. decreasing) the value of one or more features must…
SAT-Based Rigorous Explanations for Decision Lists
Alexey Ignatiev, Joao Marques-Silva
Decision lists (DLs) find a wide range of uses for classification problems in Machine Learning (ML), being implemented in a number of ML frameworks. DLs are often perceived as inte…
A Scalable Two Stage Approach to Computing Optimal Decision Sets
Alexey Ignatiev, Edward Lam, Peter J. Stuckey +1
Machine learning (ML) is ubiquitous in modern life. Since it is being deployed in technologies that affect our privacy and safety, it is often crucial to understand the reasoning b…