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Found 13 papers, 4 papers with code
Locally-Minimal Probabilistic Explanations
Formal abductive explanations offer crucial guarantees of rigor and so are of interest in high-stakes uses of machine learning (ML).
The Pros and Cons of Adversarial Robustness
The importance of ML model robustness is illustrated for example by the existence of competitions evaluating the progress of robustness tools, namely in the case of neural networks (NNs) but also by efforts towards robustness certification.
Axiomatic Aggregations of Abductive Explanations
We also evaluate them on multiple datasets and show that these explanations are robust to the attacks that fool SHAP and LIME.
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.
On Computing Relevant Features for Explaining NBCs
Despite the progress observed with model-agnostic explainable AI (XAI), it is the case that model-agnostic XAI can produce incorrect explanations.
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.
On Explaining Random Forests with SAT
Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers.
On Explaining Decision Trees
Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models.
