38 citations · 80 across the 9 of their papers we have counts for
10 papers
ExplAIner: A Declarative Query Language for Explaining Classification Models
Marcelo Arenas, Pablo Barceló, Diego Bustamante +3
The XAI community has studied a wide range of queries and scores for explaining predictions of ML models. From a data management perspective, this proliferation of explanation noti…
When is the Computation of a Feature Attribution Method Tractable?
P. Barceló, R. Cominetti, M. Morgado
Feature attribution methods have become essential for explaining machine learning models. Many popular approaches, such as SHAP and Banzhaf values, are grounded in power indices fr…
No Agreement Without Loss: Learning and Social Choice in Peer Review
Pablo Barceló, Mauricio Duarte, Cristóbal Rojas +1
In peer review systems, reviewers are often asked to evaluate various features of submissions, such as technical quality or novelty. A score is given to each of the predefined feat…
On Computing Probabilistic Explanations for Decision Trees
Marcelo Arenas, Pablo Barceló, Miguel Romero +1
Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of the…
Foundations of Symbolic Languages for Model Interpretability
Marcelo Arenas, Daniel Baez, Pablo Barceló +2
Several queries and scores have recently been proposed to explain individual predictions over ML models. Given the need for flexible, reliable, and easy-to-apply interpretability m…
Graph Neural Networks with Local Graph Parameters
Pablo Barceló, Floris Geerts, Juan Reutter +1
Various recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between -tuples of vertices. The distinguishing power of these "…
On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results
Marcelo Arenas, Pablo Barceló, Leopoldo Bertossi +1
In Machine Learning, the -score is a version of the Shapley value that is used to explain the result of a learned model on a specific entity by assigning a score to…
Model Interpretability through the Lens of Computational Complexity
Pablo Barceló, Mikaël Monet, Jorge Pérez +1
In spite of several claims stating that some models are more interpretable than others -- e.g., "linear models are more interpretable than deep neural networks" -- we still lack a…
The Complexity of Counting Problems over Incomplete Databases
Marcelo Arenas, Pablo Barceló, Mikaël Monet
We study the complexity of various fundamental counting problems that arise in the context of incomplete databases, i.e., relational databases that can contain unknown values in th…
Counting Problems over Incomplete Databases
Marcelo Arenas, Pablo Barceló, Mikaël Monet
We study the complexity of various fundamental counting problems that arise in the context of incomplete databases, i.e., relational databases that can contain unknown values in th…