47 citations · 99 across the 17 of their papers we have counts for
18 papers
Rigorous Explanations for Tree Ensembles
Yacine Izza, Alexey Ignatiev, Xuanxiang Huang +2
Tree ensembles (TEs) find a multitude of practical applications. They represent one of the most general and accurate classes of machine learning methods. While they are typically q…
Uncovering Bugs in Formal Explainers: A Case Study with PyXAI
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev +1
Formal explainable artificial intelligence (XAI) offers unique theoretical guarantees of rigor when compared to other non-formal methods of explainability. However, little attentio…
Most General Explanations of Tree Ensembles (Extended Version)
Yacine Izza, Alexey Ignatiev, Sasha Rubin +2
Explainable Artificial Intelligence (XAI) is critical for attaining trust in the operation of AI systems. A key question of an AI system is ``why was this decision made this way''.…
Efficient Contrastive Explanations on Demand
Yacine Izza, Joao Marques-Silva
Recent work revealed a tight connection between adversarial robustness and restricted forms of symbolic explanations, namely distance-based (formal) explanations. This connection i…
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…
The Pros and Cons of Adversarial Robustness
Yacine Izza, Joao Marques-Silva
Robustness is widely regarded as a fundamental problem in the analysis of machine learning (ML) models. Most often robustness equates with deciding the non-existence of adversarial…
Locally-Minimal Probabilistic Explanations
Yacine Izza, Kuldeep S. Meel, Joao Marques-Silva
Explainable Artificial Intelligence (XAI) is widely regarding as a cornerstone of trustworthy AI. Unfortunately, most work on XAI offers no guarantees of rigor. In high-stakes doma…
Axiomatic Aggregations of Abductive Explanations
Gagan Biradar, Yacine Izza, Elita Lobo +2
The recent criticisms of the robustness of post hoc model approximation explanation methods (like LIME and SHAP) have led to the rise of model-precise abductive explanations. For e…
Delivering Inflated Explanations
Yacine Izza, Alexey Ignatiev, Peter Stuckey +1
In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently arises given a decision made by an AI system is, ``why was the decision made in this…
On Computing Probabilistic Abductive Explanations
Yacine Izza, Xuanxiang Huang, Alexey Ignatiev +3
The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches…
On Computing Relevant Features for Explaining NBCs
Yacine Izza, Joao Marques-Silva
Despite the progress observed with model-agnostic explainable AI (XAI), it is the case that model-agnostic XAI can produce incorrect explanations. One alternative are the so-called…
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…
On Tackling Explanation Redundancy in Decision Trees
Yacine Izza, Alexey Ignatiev, Joao Marques-Silva
Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-calle…
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…