resampling

Introduces FewRS, a resampling-based approach that drastically reduces the number of resampled datasets required for statistically-sound data mining, achieving up to two orders of magnitude speedup while maintaining rigorous false discovery control and high statistical power.

RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference in high-dimensional data, supporting partial correlation networks and conditional Gaussian Bayesian networks with graphlet-based topology analysis.

University of Memphis researchers propose HAMR, a model-agnostic meta-learning framework that uses bi-level optimization and neighborhood-aware resampling to adaptively reweight hard examples and minority classes across six imbalanced NLP datasets.