The paper introduces the Min-Marginal Power Index (MinPI), a game-theoretic feature selection method that labels features as harmful, neutral, or decisive based on whether adding them to a coalition drops utility below a threshold. MinPI enables conservative feature removal without a score cutoff, and its application to six benchmark datasets removed 12.5% of features with negligible loss in held‑out utility. The method also improves multi‑objective search performance by prefiltering features, especially in datasets with many removable features.
arXiv:2605. 04954v2 Announce Type: replace-cross Abstract: Per-instance algorithm selection (PIAS) takes advantage of complementarity between a set of algorithms by deciding which algorithm to run on a given instance.
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: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:2607. 01539v1 Announce Type: cross Abstract: This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can act as a credible outer-loop optimizer for classification model selection.
arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.
arXiv:2610.26380v2 Announce Type: replace-cross Abstract: This paper considers a decision-maker tasked with identifying the $k$ best items from a large pool of candidates, such as job applicants, LLM...
The paper evaluates coreset selection methods by incorporating both selection and training time into a unified wall‑clock budget, using a standardized benchmark across four datasets and multiple selectors. Across numerous budget anchors, simple random or full‑data training consistently outperforms sophisticated selectors, and selection costs are dominated by a full‑dataset scan that cannot be amortized. The study also identifies when subset reuse can justify selection and reports several correctness fixes in a popular codebase.
The paper introduces a "nominate-then-adjudicate" pipeline for tuning mixed‑integer programming (MIP) solvers, where a fixed portfolio of configurations is sampled once and reused across instances, eliminating per‑instance search at inference. A 20‑feature representation maps structurally different instances to the same vector, enabling a one‑pass, zero‑solve selection process that outperforms default settings and per‑instance baselines such as ISAC+. The approach demonstrates significant practical gains, with the adjudicator achieving 538 wins over the top nominee and improving win rates against defaults from 84.9% to 90.2%.
arXiv:2608. 02887v1 Announce Type: new Abstract: Choosing which features to collect is a deployment decision: the same limited questionnaire, test panel, or sensor set may need to serve several heterogeneous populations.
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:2508. 07952v2 Announce Type: replace Abstract: Clustering algorithms often assume all features contribute equally to the data structure, an assumption that usually fails in high-dimensional or noisy settings.
arXiv:2608. 02305v1 Announce Type: new Abstract: Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget.