Interleaved Resampling and Refitting: Data and Compute-Efficient Evaluation of Black-Box Predictors
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 training algorithm and develop an efficient procedure for estimating the excess risk. Our evaluation algorithm is both computationally and data efficient. In particular, it requires access to only a single dataset and does not rely on any additional validation data. Computationally, it only requires refitting the model on several much smaller datasets obtained through sequential resampling, in contrast to previous wild refitting methods that require full-scale retraining and might t...
| Authors | Haichen Hu, David Simchi-Levi |
|---|---|
| Primary category | cs.LG |
| Published | 15 March 2026 |
| Last updated at source | 2 April 2026 |
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