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Found 7 papers, 2 papers with code
A Uniform Language to Explain Decision Trees
The formal XAI community has studied a plethora of interpretability queries aiming to understand the classifications made by decision trees.
On Computing Probabilistic Explanations for Decision Trees
Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models.
Foundations of Symbolic Languages for Model Interpretability
Several queries and scores have recently been proposed to explain individual predictions over ML models.
Querying in the Age of Graph Databases and Knowledge Graphs
Graphs have become the best way we know of representing knowledge.
On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results
While in general computing Shapley values is an intractable problem, we prove a strong positive result stating that the $\mathsf{SHAP}$-score can be computed in polynomial time over deterministic and decomposable Boolean circuits.
The Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuits
While in general computing Shapley values is a computationally intractable problem, it has recently been claimed that the SHAP-score can be computed in polynomial time over the class of decision trees.
Exchanging OWL 2 QL Knowledge Bases
Knowledge base exchange is an important problem in the area of data exchange and knowledge representation, where one is interested in exchanging information between a source and a target knowledge base connected through a mapping.
