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Practical Coreset Constructions for Machine Learning

arXiv 1703.06476 ↗First posted 19 Mar 2017stat.MLcited by 75

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Abstract (arXiv)

We investigate coresets - succinct, small summaries of large data sets - so that solutions found on the summary are provably competitive with solution found on the full data set. We provide an overview over the state-of-the-art in coreset construction for machine learning. In Section 2, we present both the intuition behind and a theoretically sound framework to construct coresets for general problems and apply it to k-means clustering. In Section 3 we summarize existing coreset construction algorithms for a variety of machine learning problems such as maximum likelihood estimation of mixture models, Bayesian non-parametric models, principal component analysis, regression and general empirical risk minimization.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Mar 2017.

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Citing papers per year: 2021: 17; 2022: 11; 2023: 18; 2024: 11; 2025: 9; 2026: 9.

75 papers.

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  1. 2022Dataset Distillation by Matching Training Trajectoriescited by 437
  2. 2023Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspectivecited by 136
  3. 2022Dataset Distillation via Factorizationcited by 115
  4. 2023Data Distillation: A Surveycited by 86
  5. 2021Manipulating SGD with Data Ordering Attackscited by 73
  6. 2024A Survey of Multimodal Large Language Model from A Data-centric Perspectivecited by 73
  7. 2021Soft-Label Anonymous Gastric X-ray Image Distillationcited by 51
  8. 2022ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Trainingcited by 45
  9. 2021Efficient Lottery Ticket Finding: Less Data is Morecited by 44
  10. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  11. 2023Sequential Subset Matching for Dataset Distillationcited by 40
  12. 2022Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharingcited by 35
38 more
  1. 2021Coresets for Classification -- Simplified and Strengthenedcited by 28
  2. 2025A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advancescited by 28
  3. 2022Dataset Distillation Using Parameter Pruningcited by 27
  4. 2022Adapting to Online Label Shift with Provable Guaranteescited by 26
  5. 2024Dataset Distillation by Automatic Training Trajectoriescited by 26
  6. 2022Dataset Distillation for Medical Dataset Sharingcited by 25
  7. 2021Improved Regret Bound and Experience Replay in Regularized Policy Iterationcited by 18
  8. 2023Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sourcescited by 16
  9. 2024Data Readiness for AI: A 360-Degree Surveycited by 15
  10. 2021Surrogate Likelihoods for Variational Annealed Importance Samplingcited by 14
  11. 2021Adversarial Robustness of Streaming Algorithms through Importance Samplingcited by 13
  12. 2021Dimensionality Reduction for Wasserstein Barycentercited by 13
  13. 2024Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensatorcited by 13
  14. 2022Optimization with Access to Auxiliary Informationcited by 12
  15. 2021Coresets for Decision Trees of Signalscited by 10
  16. 2023Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methodscited by 10
  17. 2023A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairnesscited by 9
  18. 2021Coresets for Time Series Clusteringcited by 8
  19. 2021Data Summarization via Bilevel Optimizationcited by 7
  20. 2021Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problemscited by 6
  21. 2021Learning from Mistakes -- A Framework for Neural Architecture Searchcited by 6
  22. 2023Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imagingcited by 6
  23. 2024CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecastingcited by 6
  24. 2022Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithmcited by 5
  25. 2023FairWASP: Fast and Optimal Fair Wasserstein Pre-processingcited by 5
  26. 2024Behaviour Distillationcited by 5
  27. 2025AMPO: Active Multi-Preference Optimization for Self-play Preference Selectioncited by 5
  28. 2021One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypescited by 4
  29. 2021Partial Wasserstein Coveringcited by 4
  30. 2022Black-box Coreset Variational Inferencecited by 4
  31. 2023Does "Deep Learning on a Data Diet" reproduce? Overall yes, but GraNd at Initialization does notcited by 4
  32. 2023Coreset selection can accelerate quantum machine learning models with provable generalizationcited by 4
  33. 2023A Unified View of Differentially Private Deep Generative Modelingcited by 4
  34. 2024No Dimensional Sampling Coresets for Classificationcited by 4
  35. 2024Small coresets via negative dependence: DPPs, linear statistics, and concentrationcited by 4
  36. 2025Subsampling Graphs with GNN Performance Guaranteescited by 4
  37. 2023Fast (1+varepsilon)-Approximation Algorithms for Binary Matrix Factorizationcited by 3
  38. 2023REDUCR: Robust Data Downsampling Using Class Priority Reweightingcited by 3

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