Joao Marques-Silva

research

∙ 09/06/2023

A Refutation of Shapley Values for Explainability

Recent work demonstrated the existence of Boolean functions for which Sh...

0 Xuanxiang Huang, et al. ∙

research

∙ 06/27/2023

On Logic-Based Explainability with Partially Specified Inputs

In the practical deployment of machine learning (ML) models, missing dat...

0 Ramón Béjar, et al. ∙

research

∙ 06/27/2023

Delivering Inflated Explanations

In the quest for Explainable Artificial Intelligence (XAI) one of the qu...

0 Yacine Izza, et al. ∙

research

∙ 06/05/2023

From Robustness to Explainability and Back Again

In contrast with ad-hoc methods for eXplainable Artificial Intelligence ...

0 Xuanxiang Huang, et al. ∙

research

∙ 02/16/2023

The Inadequacy of Shapley Values for Explainability

This paper develops a rigorous argument for why the use of Shapley value...

0 Xuanxiang Huang, et al. ∙

research

∙ 12/12/2022

On Computing Probabilistic Abductive Explanations

The most widely studied explainable AI (XAI) approaches are unsound. Thi...

0 Yacine Izza, et al. ∙

research

∙ 10/27/2022

Feature Necessity Relevancy in ML Classifier Explanations

Given a machine learning (ML) model and a prediction, explanations can b...

0 Xuanxiang Huang, et al. ∙

research

∙ 07/11/2022

On Computing Relevant Features for Explaining NBCs

Despite the progress observed with model-agnostic explainable AI (XAI), ...

0 Yacine Izza, et al. ∙

research

∙ 06/20/2022

Eliminating The Impossible, Whatever Remains Must Be True

The rise of AI methods to make predictions and decisions has led to a pr...

0 Jinqiang Yu, et al. ∙

research

∙ 05/20/2022

On Tackling Explanation Redundancy in Decision Trees

Decision trees (DTs) epitomize the ideal of interpretability of machine ...

0 Yacine Izza, et al. ∙

research

∙ 05/19/2022

Provably Precise, Succinct and Efficient Explanations for Decision Trees

Decision trees (DTs) embody interpretable classifiers. DTs have been adv...

0 Yacine Izza, et al. ∙

research

∙ 02/15/2022

On Deciding Feature Membership in Explanations of SDD Related Classifiers

When reasoning about explanations of Machine Learning (ML) classifiers, ...

0 Xuanxiang Huang, et al. ∙

research

∙ 07/04/2021

Efficient Explanations for Knowledge Compilation Languages

Knowledge compilation (KC) languages find a growing number of practical ...

8 Xuanxiang Huang, et al. ∙

research

∙ 06/02/2021

On Efficiently Explaining Graph-Based Classifiers

Recent work has shown that not only decision trees (DTs) may not be inte...

0 Xuanxiang Huang, et al. ∙

research

∙ 06/01/2021

Efficient Explanations With Relevant Sets

Recent work proposed δ-relevant inputs (or sets) as a probabilistic expl...

0 Yacine Izza, et al. ∙

research

∙ 06/01/2021

Explanations for Monotonic Classifiers

In many classification tasks there is a requirement of monotonicity. Con...

0 Joao Marques-Silva, et al. ∙

research

∙ 05/21/2021

On Explaining Random Forests with SAT

Random Forest (RFs) are among the most widely used Machine Learning (ML)...

0 Yacine Izza, et al. ∙

research

∙ 05/14/2021

SAT-Based Rigorous Explanations for Decision Lists

Decision lists (DLs) find a wide range of uses for classification proble...

0 Alexey Ignatiev, et al. ∙

research

∙ 03/18/2021

MILP for the Multi-objective VM Reassignment Problem

Machine Reassignment is a challenging problem for constraint programming...

0 Takfarinas Saber, et al. ∙

research

∙ 02/03/2021

A Scalable Two Stage Approach to Computing Optimal Decision Sets

Machine learning (ML) is ubiquitous in modern life. Since it is being de...

0 Alexey Ignatiev, et al. ∙

research

∙ 12/21/2020

On Relating 'Why?' and 'Why Not?' Explanations

Explanations of Machine Learning (ML) models often address a 'Why?' ques...

0 Alexey Ignatiev, et al. ∙

research

∙ 10/21/2020

On Explaining Decision Trees

Decision trees (DTs) epitomize what have become to be known as interpret...

0 Yacine Izza, et al. ∙

research

∙ 08/13/2020

Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay

Recent work proposed the computation of so-called PI-explanations of Nai...

20 Joao Marques-Silva, et al. ∙

research

∙ 12/13/2019

From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)

This paper proposes a tentative and original survey of meeting points be...

17 Zied Bouraoui, et al. ∙

research

∙ 07/04/2019

On Validating, Repairing and Refining Heuristic ML Explanations

Recent years have witnessed a fast-growing interest in computing explana...

0 Alexey Ignatiev, et al. ∙

research

∙ 11/26/2018

Abduction-Based Explanations for Machine Learning Models

The growing range of applications of Machine Learning (ML) in a multitud...

0 Alexey Ignatiev, et al. ∙

research

∙ 07/05/2012

Generalizing Redundancy in Propositional Logic: Foundations and Hitting Sets Duality

Detection and elimination of redundant clauses from propositional formul...

0 Anton Belov, et al. ∙

research

∙ 09/13/2011

On Validating Boolean Optimizers

Boolean optimization finds a wide range of application domains, that mot...

0 Antonio Morgado, et al. ∙

research

∙ 11/09/2009

Industrial-Strength Formally Certified SAT Solving

Boolean Satisfiability (SAT) solvers are now routinely used in the verif...

0 Ashish Darbari, et al. ∙

research

∙ 10/20/2009

How to Complete an Interactive Configuration Process?

When configuring customizable software, it is useful to provide interact...

0 Mikoláš Janota, et al. ∙

research

∙ 09/30/2009

Algorithms for finding dispensable variables

This short note reviews briefly three algorithms for finding the set of ...

0 Mikoláš Janota, et al. ∙

Joao Marques-Silva

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