mlsanityData to 7 Oct 2026

A Coreset Learning Reality Check

arXiv 2301.06163 ↗First posted 15 Jan 2023cs.LG cs.AI stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 48 references that are in the map. First posted on arXiv 15 Jan 2023.

Builds on

18 of 48 references.

References · 18

  1. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  2. 2020What is the State of Neural Network Pruning?cited by 578
  3. 2020A Metric Learning Reality Checkcited by 317
  4. 2021Accounting for Variance in Machine Learning Benchmarkscited by 195
  5. 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 181
  6. 2015Should we really use post-hoc tests based on mean-ranks?cited by 116
  7. 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 111
  8. 2016Coresets for Scalable Bayesian Logistic Regressioncited by 79
  9. 2019A Step Toward Quantifying Independently Reproducible Machine Learning Researchcited by 77
  10. 2018On Coresets for Logistic Regressioncited by 65
  11. 2016New Frameworks for Offline and Streaming Coreset Constructionscited by 37
  12. 2017Optimal Subsampling for Large Sample Logistic Regressioncited by 28
6 more
  1. 2021Coresets for Classification -- Simplified and Strengthenedcited by 28
  2. 2020Research Reproducibility as a Survival Analysiscited by 14
  3. 2017Optimal Sub-sampling with Influence Functionscited by 11
  4. 2022Does the Market of Citations Reward Reproducible Work?cited by 7
  5. 2022A Siren Song of Open Source Reproducibilitycited by 7
  6. 2019On Coresets for Regularized Loss Minimizationcited by 6

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2023Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Testscited by 11
  2. 2024What Do Machine Learning Researchers Mean by "Reproducible"?cited by 8
  3. 2024High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updatescited by 3
  4. 2024Generalizability of experimental studiescited by 0