Dataset Pruning

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Dataset Pruning from First Principles: A Label-Free Linear Programming Approach
Oct 7, 2026Rodrigo Schuller, Francisco GanacimDataset Pruning Training Data Selection
Instituto de Matem´atica Pura e Aplicada (IMPA) Rio de Janeiro, Brazil
Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition
Jul 16, 2026Tong Jin, Yunpeng Liu, Shuyu Hu +3Dataset Pruning Visual Place Recognition
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China · University of Chinese Academy of Sciences, Beijing 100049, China · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China +1
From General Actions to Domain-Specific Monitoring: Prior-Adaptive Transfer for Skeleton-Based Action Recognition
Jul 3, 2026Hao Wang, Di Yang, Jiangtao WangSkeleton-Based Action Recognition Dataset Pruning
University of Science and Technology of China
Distill on a Diet: Efficient Knowledge Distillation via Learnable Data Pruning
Jun 24, 2026Yifan Wu, Yiqi Wang, Xichen Ye +5Dataset Pruning Knowledge Distillation
School of Data Science, Fudan University · Faculty of Engineering, The Chinese University of Hong Kong · School of Computer Engineering and Science, Shanghai University +1
Data Pruning: Redundant, Problematic, and Interdependent Samples
Jun 20, 2026Leon Freese, Marthinus W. TheunissenDataset Pruning Training Data Selection
Faculty of Engineering, North-West University, South Africa · Centre for Artificial Intelligence Research (CAIR), South Africa · National Institute for Theoretical and Computational Sciences (NITheCS), South Africa
Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration
Jun 11, 2026Dongyue Wu, Zilin Guo, Xiaoyu Li +4Dataset Pruning Training Data Selection
State Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China · Ant Group, Hangzhou, China
RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning
Jun 10, 2026Atif Hassan, Swanand Khare, Jiaul H. PaikDataset Pruning Training Data Selection
Department of Artificial Intelligence, IIT Kharagpur, Kharagpur, West Bengal, India · Department of Mathematics, IIT Kharagpur, Kharagpur, West Bengal, India
OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
Jun 7, 2026Chenhan Jin, Shengze Xu, Qingsong Wang +3Dataset Pruning Data-Efficient Learning
The Chinese University of Hong Kong · Xiangtan University · University of Utah +3
Joint Model and Data Sparsification via the Marginal Likelihood
May 28, 2026Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth +2Dataset Pruning Robust Regression
1UvA-Bosch Delta Lab, University of Amsterdam · 2RIKEN Center for AI Project, Tokyo, Japan · Department of Computer Science, Johns Hopkins University.
Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling
May 22, 2026Yeseul Cho, Baekrok Shin, Changmin Kang +1Dataset Pruning Coreset Selection
Kim Jaechul Graduate School of AI, KAIST
Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments
May 11, 2026Danilo Brajovic, David A. Kreplin, Marco F. HuberDataset Pruning Training Data Selection
Fraunhofer IPA, Stuttgart, Germany · Institute of Industrial Manufacturing and Engineering IFF, University of Stuttgart, Germany · Hochschule Heilbronn, Germany
Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality
Jul 11, 2025Sazzad Hossain, Ponkrshnan Thiagarajan, Shashank Pathrudkar +4Intrinsic Dimensionality Dataset Pruning
Department of Mechanical and Aerospace Engineering, Michigan Technological University, Houghton, MI, USA. · Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA. · Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA. +1

Oct 7, 2026, stat.MLJ/K move · Enter open · S save

Dataset Pruning from First Principles: A Label-Free Linear Programming Approach

Rodrigo Schuller, Francisco Ganacim

Instituto de Matem´atica Pura e Aplicada (IMPA) Rio de Janeiro, Brazil

Dataset Pruning Training Data Selection Variance Reduction

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Dataset pruning reduces a large training set to a representative subset while preserving model performance. Existing geometry-based methods typically assume that nearby points in embedding space share similar properties. Rather than imposing this assumption, we derive geometric selection criteria by reformulating unbiased subset selection as a variance minimization problem. Unbiasedness ensures that unweighted subset averages recover full-dataset averages in expectation, including losses and gradients at fixed model parameters. Specifically, we characterize a family of unbiased subset selection algorithms as a high-dimensional polytope. In this context, minimizing the expected sampling variance is a linear objective. Differences in sampling variance, averaged over rigid motions, admit closed-form pairwise expressions. Because the polytope has high dimension, directly applying standard linear programming is impractical. We instead use these expressions to construct an efficient vertex walk that optimizes an approximation of the variance objective while preserving unbiasedness, yielding a method that requires neither labels nor model training during selection. Across CIFAR-10, MNIST, and CelebA benchmarks, our method matches or exceeds uniform sampling in mean test accuracy at every evaluated budget and outperforms competing geometric methods in several settings, particularly at small selection budgets. Beyond dataset pruning, the same framework reduces stochastic-gradient variance by increasing diversity within mini-batches while keeping the batch size unchanged.

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