mlsanityData to 7 Oct 2026

Coresets and Sketches

arXiv 1601.00617 ↗First posted 4 Jan 2016cs.CGcited by 63

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Abstract (arXiv)

Geometric data summarization has become an essential tool in both geometric approximation algorithms and where geometry intersects with big data problems. In linear or near-linear time large data sets can be compressed into a summary, and then more intricate algorithms can be run on the summaries whose results approximate those of the full data set. Coresets and sketches are the two most important classes of these summaries. We survey five types of coresets and sketches: shape-fitting, density estimation, high-dimensional vectors, high-dimensional point sets / matrices, and clustering.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jan 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 10; 2023: 11; 2024: 11; 2025: 11; 2026: 12.

63 papers.

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  1. 2024LESS: Selecting Influential Data for Targeted Instruction Tuningcited by 514
  2. 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 295
  3. 2022Dataset Condensation via Efficient Synthetic-Data Parameterizationcited by 187
  4. 2023Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Modelscited by 120
  5. 2022Efficient Dataset Distillation Using Random Feature Approximationcited by 111
  6. 2024DsDm: Model-Aware Dataset Selection with Datamodelscited by 103
  7. 2024How to Train Data-Efficient LLMscited by 91
  8. 2022Dataset Condensation with Latent Space Knowledge Factorization and Sharingcited by 34
  9. 2022Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptionscited by 20
  10. 2021Oblivious sketching for logistic regressioncited by 18
  11. 2021A Unified Approach to Coreset Learningcited by 18
  12. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
38 more
  1. 2025FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gaincited by 14
  2. 2022p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresetscited by 11
  3. 2024FairDeDup: Detecting and Mitigating Vision-Language Fairness Disparities in Semantic Dataset Deduplicationcited by 11
  4. 2021Coresets for Decision Trees of Signalscited by 10
  5. 2023Preemptively Pruning Clever-Hans Strategies in Deep Neural Networkscited by 9
  6. 2024In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Modelscited by 9
  7. 2021Coresets for Time Series Clusteringcited by 8
  8. 2023Almost Linear Constant-Factor Sketching for ℓ1 and Logistic Regressioncited by 8
  9. 2023Federated Wasserstein Distancecited by 8
  10. 2021A Novel Sequential Coreset Method for Gradient Descent Algorithmscited by 7
  11. 2022Coresets for Data Discretization and Sine Wave Fittingcited by 6
  12. 2022Obstacle Aware Sampling for Path Planningcited by 6
  13. 2021Introduction to Coresets: Approximated Meancited by 5
  14. 2023Deep Active Learning in the Presence of Label Noise: A Surveycited by 5
  15. 2023AutoCoreset: An Automatic Practical Coreset Construction Frameworkcited by 4
  16. 2024No Dimensional Sampling Coresets for Classificationcited by 4
  17. 2024Dataset Distillation for Histopathology Image Classificationcited by 4
  18. 2024Turnstile ℓp leverage score sampling with applicationscited by 3
  19. 2024Supervised Kernel Thinningcited by 3
  20. 2025Core-Set Selection for Data-efficient Land Cover Segmentationcited by 3
  21. 2021Sketching Merge Trees for Scientific Data Visualizationcited by 2
  22. 2021NNK-Means: Data summarization using dictionary learning with non-negative kernel regressioncited by 2
  23. 2024Error-driven Data-efficient Large Multimodal Model Tuningcited by 2
  24. 2025Provably faster randomized and quantum algorithms for k-means clustering via uniform samplingcited by 2
  25. 2025Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curvescited by 2
  26. 2026Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Cachingcited by 2
  27. 2026Exact Risk Ratios for Weighted Data Selection in Linear Regressioncited by 2
  28. 2022Coresets for Relational Data and The Applicationscited by 1
  29. 2023A Markovian Formalism for Active Queryingcited by 1
  30. 2023Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxiescited by 1
  31. 2024Data subsampling for Poisson regression with pth-root-linkcited by 1
  32. 2025Instance Data Condensation for Image Super-Resolutioncited by 1
  33. 2025Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selectioncited by 1
  34. 2025Investigating Data Pruning for Pretraining Biological Foundation Models at Scalecited by 1
  35. 2025Alchemist: Unlocking Efficiency in Text-to-Image Model Training via Meta-Gradient Data Selectioncited by 1
  36. 2026Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learningcited by 1
  37. 2026Distill on a Diet: Efficient Knowledge Distillation via Learnable Data Pruningcited by 1
  38. 2026Provable Pruning for Efficient 3D Gaussian Splatting via Coresetscited by 1

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