2 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale
Denys Herasymuk, Anastasiia Mozghova, Nazar Protsiv +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…
Table2Image: Lightweight Tabular Learning with Generated Proxy Representations and Reliability Diagnostics
Seungeun Lee, Kihwan Lee, Subin Bae +5
Deep tabular models should ideally balance predictive performance, parameter efficiency, and robustness to imperfect learning signals---properties that are rarely considered jointl…