AI RESEARCH
Interleaved Resampling and Refitting: Data and Compute-Efficient Evaluation of Black-Box Predictors
arXiv CS.LG
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ArXi:2603.14218v1 Announce Type: new We study the problem of evaluating the excess risk of large-scale empirical risk minimization under the square loss. Leveraging the idea of wild refitting and resampling, we assume only black-box access to the