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06/27/2023
Delivering Inflated Explanations
In the quest for Explainable Artificial Intelligence (XAI) one of the qu...
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12/12/2022
On Computing Probabilistic Abductive Explanations
The most widely studied explainable AI (XAI) approaches are unsound. Thi...
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07/11/2022
On Computing Relevant Features for Explaining NBCs
Despite the progress observed with model-agnostic explainable AI (XAI), ...
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05/20/2022
On Tackling Explanation Redundancy in Decision Trees
Decision trees (DTs) epitomize the ideal of interpretability of machine ...
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05/19/2022
Provably Precise, Succinct and Efficient Explanations for Decision Trees
Decision trees (DTs) embody interpretable classifiers. DTs have been adv...
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07/04/2021
Efficient Explanations for Knowledge Compilation Languages
Knowledge compilation (KC) languages find a growing number of practical ...
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06/02/2021
On Efficiently Explaining Graph-Based Classifiers
Recent work has shown that not only decision trees (DTs) may not be inte...
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06/01/2021
Efficient Explanations With Relevant Sets
Recent work proposed δ-relevant inputs (or sets) as a probabilistic expl...
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05/21/2021
On Explaining Random Forests with SAT
Random Forest (RFs) are among the most widely used Machine Learning (ML)...
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10/21/2020