A new two-sample inference method trains a distinguisher on real and classifier-generated data to produce asymptotically valid tests for whether a black-box classifier matches the true conditional distribution.
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Evaluating Black-Box Classifiers via Stable Adaptive Two-Sample Inference
A new two-sample inference method trains a distinguisher on real and classifier-generated data to produce asymptotically valid tests for whether a black-box classifier matches the true conditional distribution.