DR-IS selects low-contamination subsets via bounded rank-disagreement in proxy ensembles under an ε-contamination model, with O(√(log(N/δ)/K)) concentration rates that certify separation when the expectation gap Δ' is positive.
Scipy 1.0: fundamental algorithms for scientific computing in python.Nature methods, 17(3):261–272
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Disagreement-Regularized Importance Sampling for Adversarial Label Corruption
DR-IS selects low-contamination subsets via bounded rank-disagreement in proxy ensembles under an ε-contamination model, with O(√(log(N/δ)/K)) concentration rates that certify separation when the expectation gap Δ' is positive.