arXiv:2606. 01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures.
By Amanda S Barnard
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.
By Wenjie Shi, Chi Zhao
The paper investigates two strategies for improving large language model (LLM) evaluation: specialized judge weights and rule‑based deferral policies. Experiments on nearly 100,000 rubric‑conditioned samples show that correct rubrics boost accuracy, while incorrect ones hurt it, and that splitting training data into criterion‑specific experts can severely degrade performance unless the experts are warm‑started from a unified model. The authors demonstrate that lightweight deferral cascades can match or exceed the accuracy of larger standalone judges at a fraction of the compute cost, and they provide practical design rules for building efficient, reliable LLM evaluators.
By Ye Chen, Weining Zhang
arXiv:2606. 21641v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been proposed as hyperparameter-optimization (HPO) advisors that "warm-start" search from prior knowledge, proposing strong configurations in very few evaluations.
By Carson Rodrigues, Oysturn Vas, Isaiah Abner DCosta, Nithish Kumar Prabhakaran
SelectArena is a new benchmark for feature selection on real-world tabular data, evaluating 15 popular methods across 71 curated datasets for both classification and regression tasks. The study uses standardized protocols and modern foundation models, revealing trade-offs among predictive performance, runtime, stability, and validity. Tree-based methods (CART, RFImportance) and the F-Test emerge as Pareto-optimal choices, while search-based methods perform poorly in speed and stability.
By Bastian Sch\"afer, Dominika Matus, Lennart Purucker, Ricardo Knauer, Erik Rodner, Frank Hutter, Anton Sokolov, Arnaud Liefooghe
The paper introduces TGL-NSGA-II, a low‑fidelity framework that uses a pretrained teacher to stratify samples by difficulty and class, then applies a short knowledge‑distillation step (KD‑Lite) before scoring candidates on a stratified evaluation set. The teacher‑guided scores are fused with a Gaussian‑process surrogate to select candidates for full evaluation, and the method is evaluated on keyword spotting and bird‑call classification tasks. Results show high Kendall‑τ values (0.74 and 0.62), a 41% reduction in proxy‑score variance, and improved hypervolume and false‑positive rates compared to full NSGA‑II, while running 2.2× faster under a constrained evaluation budget.
By Soumen Garai, Suman Samui