Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
Conformal prediction (CP) provides powerful, distribution-free prediction sets, but its guarantees rely on the exchangeability of training and test data, which is often violated in practice due to covariate shifts. While weighted conformal prediction (WCP) is designed to handle such shifts, it can suffer from significant undercoverage when the density ratio between the distributions is unbounded and/or must be learned. This is because of both overfitting in learning the density ratio, and high variance in estimating the nonconformity score threshold. To address this, we introduce clipped least-squares importance fitting (CLISF) as a reduced-variance method for density ratio estimation. Sp...
| Authors | James Wang, Surbhi Goel |
|---|---|
| Primary category | cs.LG |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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