Search Results for author:
Found 25 papers, 9 papers with code
Anytime Approximate Formal Feature Attribution
A recent alternative is so-called formal feature attribution (FFA), which defines feature importance as the fraction of formal abductive explanations (AXp's) containing the given feature.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
On Formal Feature Attribution and Its Approximation
For instance and besides the scalability limitation, the formal approach is unable to tackle the feature attribution problem.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+3
Delivering Inflated Explanations
Given a set of feature values for an instance to be explained, and a resulting decision, a formal abductive explanation is a set of features, such that if they take the given value will always lead to the same decision.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
On Computing Probabilistic Abductive Explanations
One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness.
Eliminating The Impossible, Whatever Remains Must Be True
It also means the "why not" explanations may be suspect as the counterexamples they rely on may not be meaningful.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
On Tackling Explanation Redundancy in Decision Trees
The belief in DT interpretability is justified by the fact that explanations for DT predictions are generally expected to be succinct.
Provably Precise, Succinct and Efficient Explanations for Decision Trees
The paper proposes two logic encodings for computing smallest {\delta}-relevant sets for DTs.
Efficient Explanations for Knowledge Compilation Languages
Knowledge compilation (KC) languages find a growing number of practical uses, including in Constraint Programming (CP) and in Machine Learning (ML).
On Efficiently Explaining Graph-Based Classifiers
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.
Efficient Explanations With Relevant Sets
Recent work proposed $\delta$-relevant inputs (or sets) as a probabilistic explanation for the predictions made by a classifier on a given input.
Explanations for Monotonic Classifiers
This paper describes novel algorithms for the computation of one formal explanation of a (black-box) monotonic classifier.
SAT-Based Rigorous Explanations for Decision Lists
Unfortunately, and in clear contrast with the case of DTs, this paper shows that computing explanations for DLs is computationally hard.
A Scalable Two Stage Approach to Computing Optimal Decision Sets
Machine learning (ML) is ubiquitous in modern life.
Constraint-Driven Explanations of Black-Box ML Models
Modern machine learning techniques have enjoyed widespread success, but are plagued by lack of transparency in their decision making, which has led to the emergence of the field of explainable AI.
On Relating "Why?" and "Why Not?" Explanations
Explanations of Machine Learning (ML) models often address a ‘Why?’ question.
On Explaining Decision Trees
Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models.
Optimal Decision Lists using SAT
Decision lists are one of the most easily explainable machine learning models.
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
In contrast, we show that the computation of one PI-explanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers.
Computing Optimal Decision Sets with SAT
Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large.
On Relating Explanations and Adversarial Examples
The importance of explanations (XP's) of machine learning (ML) model predictions and of adversarial examples (AE's) cannot be overstated, with both arguably being essential for the practical success of ML in different settings.
On Validating, Repairing and Refining Heuristic ML Explanations
Recent years have witnessed a fast-growing interest in computing explanations for Machine Learning (ML) models predictions.
Abduction-Based Explanations for Machine Learning Models
The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions.
On Cryptographic Attacks Using Backdoors for SAT
Propositional satisfiability (SAT) is at the nucleus of state-of-the-art approaches to a variety of computationally hard problems, one of which is cryptanalysis.
Propositional Abduction with Implicit Hitting Sets
Propositional abduction is a restriction of abduction to the propositional domain, and complexity-wise is in the second level of the polynomial hierarchy.
