47 citations · 179 across the 22 of their papers we have counts for
7 papers · 1 filter
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation
Yacine Izza, Xuanxiang Huang, Antonio Morgado +3
The uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-ma…
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…
On Relating 'Why?' and 'Why Not?' Explanations
Alexey Ignatiev, Nina Narodytska, Nicholas Asher +1
Explanations of Machine Learning (ML) models often address a 'Why?' question. Such explanations can be related with selecting feature-value pairs which are sufficient for the predi…
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…
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…
On Validating, Repairing and Refining Heuristic ML Explanations
Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva
Recent years have witnessed a fast-growing interest in computing explanations for Machine Learning (ML) models predictions. For non-interpretable ML models, the most commonly used…