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Found 5 papers, 3 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.
The Packing Chromatic Number of the Infinite Square Grid is 15
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).
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.
Model Interpretability through the Lens of Computational Complexity
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.
