Disentangling the Roles of Representation and Selection in Data Pruning

• URL: http://arxiv.org/abs/2507.03648v1
• Date: Fri, 04 Jul 2025 15:25:04 GMT
• Title: Disentangling the Roles of Representation and Selection in Data Pruning
• Authors: Yupei Du, Yingjin Song, Hugh Mee Wong, Daniil Ignatev, Albert Gatt, Dong Nguyen,
• Abstract summary: We decompose data pruning into two key components: the data representation and the selection algorithm.<n>Our theoretical and empirical results highlight the crucial role of representations.<n>Different selection algorithms excel in different settings, and none consistently outperforms the others.
• Score: 6.141776277655227
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data pruning, selecting small but impactful subsets, offers a promising way to efficiently scale NLP model training. However, existing methods often involve many different design choices, which have not been systematically studied. This limits future developments. In this work, we decompose data pruning into two key components: the data representation and the selection algorithm, and we systematically analyze their influence on the selection of instances. Our theoretical and empirical results highlight the crucial role of representations: better representations, e.g., training gradients, generally lead to a better selection of instances, regardless of the chosen selection algorithm. Furthermore, different selection algorithms excel in different settings, and none consistently outperforms the others. Moreover, the selection algorithms do not always align with their intended objectives: for example, algorithms designed for the same objective can select drastically different instances, highlighting the need for careful evaluation.

Related papers

• Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model [0.20718016474717196]
  We conduct a benchmark study using 17 different classifiers and three types of trajectory on a classification task using the BBOB benchmark suite.<n>We find that the choice of classifier has a significant impact, showing that feature-based and interval-based models are the best choices.
  arXiv  Detail & Related papers  (2025-01-20T11:28:45Z)
• DsDm: Model-Aware Dataset Selection with Datamodels [81.01744199870043]
  Standard practice is to filter for examples that match human notions of data quality. We find that selecting according to similarity with "high quality" data sources may not increase (and can even hurt) performance compared to randomly selecting data. Our framework avoids handpicked notions of data quality, and instead models explicitly how the learning process uses train datapoints to predict on the target tasks.
  arXiv  Detail & Related papers  (2024-01-23T17:22:00Z)
• A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning [9.596923373834093]
  We propose a novel filter feature selection method, ContrastFS, which selects discriminative features based on the discrepancies features shown between different classes. We validate effectiveness and efficiency of our approach on several widely studied benchmark datasets, results show that the new method performs favorably with negligible computation.
  arXiv  Detail & Related papers  (2024-01-15T05:32:35Z)
• Towards Free Data Selection with General-Purpose Models [71.92151210413374]
  A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. Current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. FreeSel bypasses the heavy batch selection process, achieving a significant improvement in efficiency and being 530x faster than existing active learning methods.
  arXiv  Detail & Related papers  (2023-09-29T15:50:14Z)
• Fair Feature Subset Selection using Multiobjective Genetic Algorithm [0.0]
  We present a feature subset selection approach that improves both fairness and accuracy objectives. We use statistical disparity as a fairness metric and F1-Score as a metric for model performance. Our experiments on the most commonly used fairness benchmark datasets show that using the evolutionary algorithm we can effectively explore the trade-off between fairness and accuracy.
  arXiv  Detail & Related papers  (2022-04-30T22:51:19Z)
• Compactness Score: A Fast Filter Method for Unsupervised Feature Selection [66.84571085643928]
  We propose a fast unsupervised feature selection method, named as, Compactness Score (CSUFS) to select desired features. Our proposed algorithm seems to be more accurate and efficient compared with existing algorithms.
  arXiv  Detail & Related papers  (2022-01-31T13:01:37Z)
• Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes [43.71112693633952]
  We introduce a multiple-criteria based active learning algorithm, which incorporates three complementary criteria, i.e., informativeness, representativeness and diversity. We show that our method performs significantly better and is more stable than other multiple-criteria based AL algorithms.
  arXiv  Detail & Related papers  (2021-07-04T13:22:54Z)
• Auto-weighted Multi-view Feature Selection with Graph Optimization [90.26124046530319]
  We propose a novel unsupervised multi-view feature selection model based on graph learning. The contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T03:25:25Z)
• Greedy Search Algorithms for Unsupervised Variable Selection: A Comparative Study [3.4888132404740797]
  This paper focuses on unsupervised variable selection based dimensionality reduction. We present a critical evaluation of seven unsupervised greedy variable selection algorithms. We introduce and evaluate for the first time, a lazy implementation of the variance explained based forward selection component analysis (FSCA) algorithm.
  arXiv  Detail & Related papers  (2021-03-03T21:10:26Z)
• Online Active Model Selection for Pre-trained Classifiers [72.84853880948894]
  We design an online selective sampling approach that actively selects informative examples to label and outputs the best model with high probability at any round. Our algorithm can be used for online prediction tasks for both adversarial and streams.
  arXiv  Detail & Related papers  (2020-10-19T19:53:15Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.