arXiv Machine Learning
CID: Measuring Feature Importance Through Counterfactual Distributions
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arXiv AI
1d ago
Out-of-Distribution Detection using Counterfactual Distance
arXiv:2508.10148v2 Announce Type: replace-cross Abstract: Accurate and explainable out-of-distribution (OOD) detection is required to use machine learning systems safely. Previous work has shown that...
arXiv AI
Sep 2
Assessing Alignment and Stability of Feature Importance Explanations via Weight of Evidence
The paper introduces a hypothesis‑testing framework that embeds feature importance methods (FIMs) within a Weight of Evidence (WoE) analysis. By quantifying how strongly observed evidence supports a given hypothesis—whether from domain knowledge, ground truth, or the FIM itself—the approach evaluates FIM alignment and variability. The authors provide theoretical links between WoE and attribution variance and demonstrate the method on LIME and SHAP explanations across varied reference hypotheses.
arXiv AI
Aug 5
Measuring Explainer Stability via Attribution Separability
arXiv:2608. 02697v1 Announce Type: cross Abstract: Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models.
arXiv Machine Learning
Jul 21
MinShap: A Shapley-Based Framework for Feature Redundancy
arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.
arXiv Machine Learning
Jul 28
An Empirical Study of Feature Selection Granularity
arXiv:2607. 24145v1 Announce Type: new Abstract: Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution better, or with respect to the performance on a downstream task.
arXiv Machine Learning
Sep 18
Null importance: Disentangling relevance for interpretable machine learning
The paper introduces a unified framework called null importance to clarify different notions of feature relevance in interpretable machine learning. It defines null importance at the population level for various relevance concepts—marginal, conditional, predictive risk, functional invariance, and causal effects—and demonstrates how each answers distinct scientific questions. Through theoretical analysis, simulations, and case studies on fairness and genomic modeling, the authors show when these null notions coincide or diverge and how different importance methods target them.