The paper proposes a new meta‑learning representation for hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization (CASH) that encodes each configuration by its historical performance across prior tasks, rather than by explicit hyperparameter values. This performance‑based representation can be used with standard surrogate models, such as linear Bayesian ridge regression, and is applied to a diversity‑based re‑ranking criterion (maximal marginal relevance) as a posterior‑free acquisition rule. Experiments on the TabArena benchmark show that this approach outperforms dedicated meta‑learning baselines, achieving comparable tuned performance in about half the number of trials, and remains competitive for single‑model HPO while scaling independently of search‑space dimensionality.
By Emiel Witting, Cheng Yan, Tom Julian Viering
arXiv:2607. 09739v1 Announce Type: new Abstract: We study LLM benchmark coreset selection: selecting a small subset of prompts over multiple benchmarks whose induced model scores and rankings approximate those obtained from the full benchmark suite.
By Jihan Yao, Gantavya Bhatt, Arnav Das, Peter Jin, Ke Bao, Qiaolin Yu, Khushi Bhardwaj, Chang Su, Jialei Wang, Yikai Zhu, Sugam Devare, Damon Mosk-Aoyama, Zhen Dong, Venkat Krishna Srinivasan, Yineng Zhang, Oleksii Kuchaiev, Jiantao Jiao, Banghua Zhu, Jeff Bilmes
arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.
By Rostislav Gusev, Alexey Zaytsev
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
arXiv:2506. 07673v2 Announce Type: replace Abstract: Large language model (LLM) evaluation is increasingly costly, prompting interest in methods that speed up evaluation by shrinking benchmark datasets.
By Guanhua Zhang, Florian E. Dorner, Moritz Hardt
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
arXiv:2610.28556v1 Announce Type: cross
Abstract: Large language models (LLMs) can leverage semantic feature names alongside summary statistics for tabular feature selection, yet it remains unclear w...
By Fateme Memar, Dongjie Wang, Arvin Agah
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.
By Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi, Muhammad Rajabinasab, Arthur Zimek
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
arXiv:2605. 07096v2 Announce Type: replace Abstract: Evaluating a new model on an existing benchmark is often necessary to understand its behavior before deployment.
By Hayden Helm, Ben Johnson, Carey Priebe
arXiv:2602. 05786v3 Announce Type: replace Abstract: Tree-boosting is a widely used machine learning technique for tabular data.
By Floris Jan Koster, Fabio Sigrist
arXiv:2606. 29328v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) typically treats context selection as ranking chunks against a single query embedding.
By Bingxue Zhang, Jianying Jia, Feida Zhu