1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution
Mateusz Gajewski, Mikołaj Morzy, Adam Karczmarz +1
Existing feature attribution methods like SHAP often suffer from global dependence, failing to capture true local model behavior. This paper introduces VARSHAP, a novel model-agnos…
EXALT: EXplainable ALgorithmic Tools for Optimization Problems
Zuzanna Bączek, Michał Bizoń, Aneta Pawelec +1
Algorithmic solutions have significant potential to improve decision-making across various domains, from healthcare to e-commerce. However, the widespread adoption of these solutio…
Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery
Mateusz Olko, Mateusz Gajewski, Joanna Wojciechowska +3
Neural causal discovery methods have recently improved in terms of scalability and computational efficiency. However, our systematic evaluation highlights significant room for impr…
Accurate estimation of feature importance faithfulness for tree models
Mateusz Gajewski, Adam Karczmarz, Mateusz Rapicki +1
In this paper, we consider a perturbation-based metric of predictive faithfulness of feature rankings (or attributions) that we call PGI squared. When applied to decision tree-base…