1 code implementation • 18 Oct 2023 • Marcelo Arenas, Pablo Barcelo, Diego Bustamante, Jose Caraball, Bernardo Subercaseaux
The formal XAI community has studied a plethora of interpretability queries aiming to understand the classifications made by decision trees.
1 code implementation • 23 Jan 2023 • Bernardo Subercaseaux, Marijn J. H. Heule
A packing $k$-coloring is a natural variation on the standard notion of graph $k$-coloring, where vertices are assigned numbers from $\{1, \ldots, k\}$, and any two vertices assigned a common color $c \in \{1, \ldots, k\}$ need to be at a distance greater than $c$ (as opposed to $1$, in standard graph colorings).
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 • NeurIPS 2020 • Pablo Barceló, Mikaël Monet, Jorge Pérez, Bernardo Subercaseaux
We prove that this notion provides a good theoretical counterpart to current beliefs on the interpretability of models; in particular, we show that under our definition and assuming standard complexity-theoretical assumptions (such as P$\neq$NP), both linear and tree-based models are strictly more interpretable than neural networks.