9 papers
RelShap: Relationally Consistent Shapley Explanations
Seungeun Lee, Joao Fonseca, Julia Stoyanovich
Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints. Widely used Shapley value-based feature attributio…
ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations
Joao Fonseca, Julia Stoyanovich
Computing the importance of features in supervised classification tasks is critical for model interpretability. Shapley values are a widely used approach for explaining model predi…
ShaRP: Explaining Rankings and Preferences with Shapley Values
Venetia Pliatsika, Joao Fonseca, Kateryna Akhynko +2
Algorithmic decisions in critical domains such as hiring, college admissions, and lending are often based on rankings. Given the impact of these decisions on individuals, organizat…
Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation
Falaah Arif Khan, Denys Herasymuk, Nazar Protsiv +1
Data missingness is a practical challenge of sustained interest to the scientific community. In this paper, we present Shades-of-Null, an evaluation suite for responsible missing v…
VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale
Denys Herasymuk, Nazar Protsiv, Anastasiia Mozghova +2
Developing machine learning (ML) systems for real-world deployment requires navigating context-dependent trade-offs among accuracy, fairness, stability, and other objectives. Exist…
SHAP-based Explanations are Sensitive to Feature Representation
Hyunseung Hwang, Andrew Bell, Joao Fonseca +3
Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an ``interpretable'' feature representatio…