Ensemble Cross-Validation

Overview

This website summarizes a collection of specialized ensemble cross-validation methods.

Check out Projects for a full list of related methods and their reproducing codes.

References

  1. Pratik Patil*, Jin-Hong Du*, and Arun Kumar Kuchibhotla, “Bagging in overparameterized learning: Risk characterization and risk monotonization”. Journal of Machine Learning Research (2023). [JMLR] [arXiv]
  2. Jin-Hong Du, Pratik Patil, Kathryn Roeder, and Arun Kumar Kuchibhotla, “Extrapolated cross-validation for randomized ensembles”. In: Journal of Computational and Graphical Statistics (2023). [DOI] [arXiv]
  3. Jin-Hong Du*, Pratik Patil*, and Arun Kumar Kuchibhotla. “Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation”. In: Proceedings of the 40th International Conference on Machine Learning. 23–29 Jul 2023. (oral) [PMLR] [arXiv]
  4. Pratik Patil, and Jin-Hong Du, “Generalized equivalences between subsampling and ridge regularization”. In: Thirty-seventh Conference on Neural Information Processing Systems. 2023. [arXiv]
  5. †Pierre C. Bellec, Jin-Hong Du, Takuya Koriyama, Pratik Patil, and Kai Tan. “Corrected generalized cross-validation for finite ensembles of penalized estimators”. 2023. [arXiv]

* Equal contributions.
† Alphabetically ordered.