arXiv Machine Learning
Jul 28
Variable Importance Identification Through Lazy Training for Binary Classification
arXiv:2607. 22979v1 Announce Type: cross Abstract: Deep neural networks have been widely used in many applications (e.
Conditional Local Importance by Quantile Expectations
arXiv:2411. 08821v4 Announce Type: replace-cross Abstract: Global variable importance measures are commonly used to interpret the results of machine learning models.
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arXiv AI
Jul 21
K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data
arXiv:2607. 16478v1 Announce Type: cross Abstract: Oversampling is widely used to address class imbalance in tabular classification, but existing methods can distort the feature importance ranking underlying model explanations.
arXiv Machine Learning
Jun 10
Correcting Variable Importance Scored by Random Forests
arXiv:2606. 10770v1 Announce Type: cross Abstract: Variable importance produced by Random Forests (RF) is used widely in statistical data analysis, and has played an important role in a variety of tasks such as assisting model interpretation, model selection and diagnosis, and cost-bounded learning etc.
arXiv Machine Learning
4d ago
LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction
arXiv:2609.36396v1 Announce Type: cross Abstract: As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical cha...
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
Jun 11
Provable Recovery of Locally Important Signed Features and Interactions from Random Forest
arXiv:2512. 11081v2 Announce Type: replace-cross Abstract: Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their interactions in complex prediction models.
arXiv Machine Learning
Sep 23
CID: Measuring Feature Importance Through Counterfactual Distributions
arXiv:2511.15371v3 Announce Type: replace Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous m...
arXiv AI
2d ago
MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees
arXiv:2610.01641v1 Announce Type: cross Abstract: Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive...
arXiv Statistics ML
Sep 14
Semiparametric Inference for Conditional Shapley Feature Importance
arXiv:2609.10313v1 Announce Type: cross Abstract: Shapley values are widely used for post-hoc feature attribution, but most estimators return point quantities and do not quantify uncertainty, and pop...
arXiv Machine Learning
Sep 10
Learning Kernels by Alignment for Multiclass Bayes Classification
The paper introduces a framework that learns the kernel used in kernel methods through alignment, leveraging the Collaborative Learning and Inference (CLaI) approach. It demonstrates that CLaI can be interpreted as a kernel alignment process and that its inference stage is equivalent to kernel Bayes classification with Parzen-window density estimation. By replacing cosine similarity with a learned Mahalanobis distance, the authors extend CLaI to multiclass classification, achieving higher accuracy, faster convergence, and lower calibration error on datasets such as CIFAR-10, PathMNIST, and SleepEDF, while also showing connections to Gaussian processes and competitive calibration in sepsis prediction.
arXiv Machine Learning
Sep 22
Multivariate quantile regression via Kolmogorov-Arnold Networks
arXiv:2609.23906v1 Announce Type: new Abstract: This paper introduces a novel algorithm for predicting conditional joint distributions of vector-valued targets in stochastic systems whose randomness...
arXiv Machine Learning
Sep 16
Observational Multiplicity
The paper introduces the concept of observational multiplicity, where multiple probabilistic classifiers can perform similarly yet produce conflicting predictions, undermining interpretability and safety. It proposes measuring this arbitrariness through a regret metric that captures how predictions could shift with different training labels. The authors present a general method to estimate regret, show it varies across dataset groups, and discuss its use for safety via abstention and targeted data collection.