References
==========

Research Papers
---------------

**ET-COME Method Papers:**
- ET-COME: Equilibrium Transport with Conformal Minority Expansion
  - Submitted to NeurIPS 2026

**Related Work:**

- Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002).
  SMOTE: Synthetic Minority Over-sampling Technique. *Journal of Artificial Intelligence Research*, 16, 321-357.

- Breiman, L. (1996).
  Bagging Predictors. *Machine Learning*, 24(2), 123-140.

- He, H., & Garcia, E. A. (2009).
  Learning from Imbalanced Data. *IEEE Transactions on Knowledge and Data Engineering*, 21(9), 1263-1284.

External Resources
------------------

**Repository:**
- Main Repository: https://github.com/Pawlo77/ET-COME
- Issue Tracker: https://github.com/Pawlo77/ET-COME/issues
- Discussions: https://github.com/Pawlo77/ET-COME/discussions

**Package:**
- PyPI: https://pypi.org/project/et-come/

**Related Projects:**
- scikit-learn: https://scikit-learn.org/
- imbalanced-learn: https://imbalanced-learn.org/

Datasets Used in Experiments
-----------------------------

Experiments utilize publicly available datasets from:
- UCI Machine Learning Repository
- Kaggle
- scikit-learn built-in datasets

For detailed dataset descriptions and download instructions, please refer to the experiments documentation in the repository.