arXiv Machine Learning

Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices

arXiv:2606. 07068v1 Announce Type: new Abstract: Background: Since 1990 many feature selection methods have been proposed across heterogeneous applications.

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.

By Muhammad Rajabinasab, Arthur Zimek

benchmarks

arXiv Machine Learning

Jul 2

Explaining Tabular Foundation Model Differences Through Meta-Features

arXiv:2605. 28418v3 Announce Type: replace Abstract: With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular dataset remains difficult.

By Markus Herre, Andrej Tschalzev, Sascha Marton, Christian Bartelt

benchmarks

arXiv Machine Learning

23h ago

Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training

arXiv:2608. 16926v1 Announce Type: new Abstract: Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance.

By Peng Sun, Yi Yang, Antong Zhang, Chunxiao Li, Yanbo Wang, Dianbo Liu, xin chen, Kai Yu, Lu Chen, Tianfan Fu

llms reinforcement-learning fine-tuning

arXiv AI

Jul 22

Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling

arXiv:2607. 18515v1 Announce Type: cross Abstract: This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machine learning model-feature combinations for predictive tasks in fused deposition modeling (FDM) process datasets.

By Saleh Valizadeh Sotubadi, Nazanin Mahjourian, Vinh Nguyen

reinforcement-learning data-engineering

arXiv Machine Learning

Jul 23

Comparing Model-agnostic Feature Selection Methods through Relative Efficiency

arXiv:2508. 14268v2 Announce Type: replace-cross Abstract: Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest.

By Chenghui Zheng, Garvesh Raskutti

benchmarks

arXiv AI

Jun 10

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.

By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu

llms benchmarks safety

arXiv AI

Aug 12

Measuring Semantic Abstractness of SAE Features via Nonlocality

arXiv:2608. 10537v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have helped uncover mechanistic explanations for LLM behaviours such as reasoning, jailbreaking etc.

By Chuqiao Lin, Shivaji Sondhi, Xiao-Liang Qi

llms safety

arXiv Machine Learning

Jul 30

Understanding Context Sampling in TabPFN on Small Tabular Datasets

arXiv:2607. 26628v1 Announce Type: new Abstract: TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts test labels without gradient updates.

By Mohammed Abdullah

llms

arXiv AI

Jun 4

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning

arXiv:2605. 23595v2 Announce Type: replace-cross Abstract: The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released models on unseen and unlabeled data.

By Trinh Pham, Viet Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen

fine-tuning benchmarks

arXiv Machine Learning

Jun 2

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

arXiv:2510. 06048v4 Announce Type: replace Abstract: Effective data selection is essential for pretraining large language models (LLMs), enhancing efficiency and improving generalization to downstream tasks.

By Jie Hao, Rui Yu, Wei Zhang, Huixia Wang, Jie Xu, Mingrui Liu

llms benchmarks

arXiv Machine Learning

Jun 25

Model-agnostic Mitigation Strategies of Data Imbalance for Regression

arXiv:2506. 01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability.

By Jelke Wibbeke, Sebastian Rohjans, Andreas Rauh

benchmarks safety

arXiv Machine Learning

Jun 15

On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization

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.

By Koen van der Blom, Diederick Vermetten

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