mlsanityData to 14 Oct 2026

Data Twinning

arXiv 2110.02927 ↗First posted 6 Oct 2021stat.ML cs.LGcited by 10

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Abstract (arXiv)

In this work, we develop a method named Twinning, for partitioning a dataset into statistically similar twin sets. Twinning is based on SPlit, a recently proposed model-independent method for optimally splitting a dataset into training and testing sets. Twinning is orders of magnitude faster than the SPlit algorithm, which makes it applicable to Big Data problems such as data compression. Twinning can also be used for generating multiple splits of a given dataset to aid divide-and-conquer procedures and k-fold cross validation.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

4 of 28 references.

References · 4

  1. 2016Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)cited by 44
  2. 2016Support pointscited by 39
  3. 2020SPlit: An Optimal Method for Data Splittingcited by 15
  4. 2017Information-Based Optimal Subdata Selection for Big Data Linear Regressioncited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 1; 2026: 1.

10 papers.

  1. 2022Optimal Ratio for Data Splittingcited by 18
  2. 2022Risk-Aware Control and Optimization for High-Renewable Power Gridscited by 7
  3. 2023A Global-Local Approximation Framework for Large-Scale Gaussian Process Modelingcited by 2
  4. 2023Band-gap regression with architecture-optimized message-passing neural networkscited by 2
  5. 2022Statistical process monitoring of artificial neural networkscited by 1
  6. 2023Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splitscited by 1
  7. 2025Density-Aware Farthest Point Samplingcited by 1
  8. 2024A model-free subdata selection method for classificationcited by 0
  9. 2024Double Machine Learning for Adaptive Causal Representation in High-Dimensional Datacited by 0
  10. 2026Starting Off on the Wrong Foot: Pitfalls in Data Preparationcited by 0