arXiv:2306. 14851v5 Announce Type: replace-cross Abstract: Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between covariates and the response in an interpretable manner.
arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.
The paper introduces LIDFS, an unsupervised feature selection technique that uses Local Intrinsic Dimensionality (LID) to rank features based on their local discriminability. By treating each feature as a point and estimating its LID via maximum likelihood from distances to its k nearest neighboring features, LIDFS assigns higher importance to features with lower LID and refines the ranking to remove locally redundant features. Experiments on 23 high‑dimensional datasets show that LIDFS outperforms both classic and state‑of‑the‑art methods in supervised and clustering evaluations while running faster than its closest competitor.
arXiv:2610.33063v1 Announce Type: cross Abstract: Deployed vision models auto-accept confident predictions, so high-confidence errors propagate silently. Maximum Softmax Probability (MSP), the standa...
arXiv:2608.23744v1 Announce Type: new Abstract: Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal...
arXiv:2602. 08913v3 Announce Type: replace Abstract: In underdetermined regression and classification problems, multiple feature subsets often yield equivalent predictive performance.
arXiv:2606. 31686v1 Announce Type: cross Abstract: Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret.
arXiv:2605.09396v2 Announce Type: replace-cross Abstract: This paper relaxes the restrictive symmetry conditions adopted in [4], [5] and extends their universal feature selection framework to accommo...
The paper evaluates how different encoding strategies and pair selection methods affect Pairwise Difference Learning (PDC) for classification. It compares four encoders—including a new triplet with absolute difference—and four pair selection schemes across almost 200 datasets using several base models. The study finds that the new encoder yields the best average F1 scores, that using only upper‑triangle pairs cuts training time by about 40% with little accuracy loss, and that no single configuration dominates across all datasets.
arXiv:2609.23926v1 Announce Type: cross Abstract: Density-Ratio Rescoring (DRR) augments a classifier trained at the original class prior with a survey-raking dual score. Raking reweights the majorit...
arXiv:2607. 27027v1 Announce Type: new Abstract: Gradient-boosted trees dominate tabular machine learning, yet canonical correlation analysis has always relied on linear or neural encoders.
Post-hoc out-of-distribution (OOD) detection is mature on far-OOD yet remains weak on near-OOD. These two settings differ in their use of class-characteristic feature directions: far-OOD inputs largel...