Robust Conformalized Selection with Noisy Responses
Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug discovery, and the alignment of large language models. Nevertheless, existing methods assume clean responses on calibration data, an assumption that rarely holds in practice. In this paper, we formulate the above tasks as selecting candidates with true predicted labels or with responses exceeding certain values. We demonstrate that existing conformal selection methods fail to control the false discovery rate (FDR) or suffer from severe power loss under contaminated calibration data. To that end, we propose Robust Co...
| Authors | Chengyao Yu, Hongxin Wei, Bingyi Jing |
|---|---|
| Primary category | stat.ML |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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