mlsanityData to 7 Oct 2026

On Coresets for Logistic Regression

arXiv 1805.08571 ↗First posted 22 May 2018cs.DS cs.LG stat.MLcited by 65

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Abstract (arXiv)

Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable worst-case instances we introduce a complexity measure μ(X), which quantifies the hardness of compressing a data set for logistic regression. μ(X) has an intuitive statistical interpretation that may be of independent interest. For data sets with bounded μ(X)-complexity, we show that a novel sensitivity sampling scheme produces the first provably sublinear (1±varepsilon)-coreset. We illustrate the performance of our method by comparing to uniform sampling as well as to state of the art methods in the area. The experiments are conducted on real world benchmark data for logistic regression.

Key facts. Cited by 65 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2018.

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Citing papers per year: 2021: 10; 2022: 11; 2023: 20; 2024: 11; 2025: 7; 2026: 6.

65 papers.

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  1. 2021Deep Learning on a Data Diet: Finding Important Examples Early in Trainingcited by 529
  2. 2023From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuningcited by 301
  3. 2022DeepCore: A Comprehensive Library for Coreset Selection in Deep Learningcited by 146
  4. 2023Dataset Distillation with Convexified Implicit Gradientscited by 46
  5. 2021Coresets for Classification -- Simplified and Strengthenedcited by 28
  6. 2025A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advancescited by 28
  7. 2023Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learningcited by 26
  8. 2022Towards Optimal Lower Bounds for k-median and k-means Coresetscited by 23
  9. 2022Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptionscited by 20
  10. 2023NLU on Data Diets: Dynamic Data Subset Selection for NLP Classification Taskscited by 19
  11. 2021Oblivious sketching for logistic regressioncited by 18
  12. 2021A Unified Approach to Coreset Learningcited by 18
38 more
  1. 2022New Coresets for Projective Clustering and Applicationscited by 16
  2. 2022Online Lewis Weight Samplingcited by 15
  3. 2021Adversarial Robustness of Streaming Algorithms through Importance Samplingcited by 13
  4. 2022Active Learning for Single Neuron Models with Lipschitz Non-Linearitiescited by 12
  5. 2022p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresetscited by 11
  6. 2023Data pruning and neural scaling laws: fundamental limitations of score-based algorithmscited by 11
  7. 2024Agnostic Active Learning of Single Index Models with Linear Sample Complexitycited by 11
  8. 2021Coresets for Decision Trees of Signalscited by 10
  9. 2023Almost Linear Constant-Factor Sketching for ℓ1 and Logistic Regressioncited by 8
  10. 2023Federated Wasserstein Distancecited by 8
  11. 2021A Novel Sequential Coreset Method for Gradient Descent Algorithmscited by 7
  12. 2022An Empirical Evaluation of k-Means Coresetscited by 7
  13. 2021Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problemscited by 6
  14. 2022Coresets for Data Discretization and Sine Wave Fittingcited by 6
  15. 2022Obstacle Aware Sampling for Path Planningcited by 6
  16. 2023Optimal Sketching Bounds for Sparse Linear Regressioncited by 6
  17. 2023Improved Active Learning via Dependent Leverage Score Samplingcited by 6
  18. 2024Scalable Learning of Item Response Theory Modelscited by 6
  19. 2021Introduction to Coresets: Approximated Meancited by 5
  20. 2023ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Explorationcited by 5
  21. 2024Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuningcited by 5
  22. 2023A Coreset Learning Reality Checkcited by 4
  23. 2023Leveraging Importance Weights in Subset Selectioncited by 4
  24. 2023Feature Space Sketching for Logistic Regressioncited by 4
  25. 2023AutoCoreset: An Automatic Practical Coreset Construction Frameworkcited by 4
  26. 2024No Dimensional Sampling Coresets for Classificationcited by 4
  27. 2025A Tight VC-Dimension Analysis of Clustering Coresets with Applicationscited by 4
  28. 2023On the Size and Approximation Error of Distilled Setscited by 3
  29. 2023Sketching Multidimensional Time Series for Fast Discord Miningcited by 3
  30. 2024Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentationcited by 3
  31. 2024Turnstile ℓp leverage score sampling with applicationscited by 3
  32. 2026CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Trainingcited by 2
  33. 2022Coresets for Relational Data and The Applicationscited by 1
  34. 2023Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outlierscited by 1
  35. 2023Simple Weak Coresets for Non-Decomposable Classification Measurescited by 1
  36. 2024A Provably Accurate Randomized Sampling Algorithm for Logistic Regressioncited by 1
  37. 2024Data subsampling for Poisson regression with pth-root-linkcited by 1
  38. 2025Improving Model Classification by Optimizing the Training Datasetcited by 1

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