1 code implementation • 18 Oct 2023 • Marcelo Arenas, Pablo Barcelo, Diego Bustamente, Jose Caraball, Bernardo Subercaseaux
The recent development of formal explainable AI has disputed the folklore claim that "decision trees are readily interpretable models", showing different interpretability queries that are computationally hard on decision trees, as well as proposing different methods to deal with them in practice.
no code implementations • 30 Jun 2022 • Marcelo Arenas, Pablo Barceló, Miguel Romero, Bernardo Subercaseaux
Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models.
1 code implementation • NeurIPS 2021 • Marcelo Arenas, Daniel Baez, Pablo Barceló, Jorge Pérez, Bernardo Subercaseaux
Several queries and scores have recently been proposed to explain individual predictions over ML models.
no code implementations • 22 Jun 2021 • Marcelo Arenas, Claudio Gutierrez, Juan F. Sequeda
Graphs have become the best way we know of representing knowledge.
no code implementations • 16 Apr 2021 • Marcelo Arenas, Pablo Barceló, Leopoldo Bertossi, Mikaël Monet
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.
no code implementations • 28 Jul 2020 • Marcelo Arenas, Pablo Barceló Leopoldo Bertossi, Mikaël Monet
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.
no code implementations • 21 Apr 2013 • Marcelo Arenas, Elena Botoeva, Diego Calvanese, Vladislav Ryzhikov
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.