CONTRA: Conformal Prediction Region via Normalizing Flow Transformation
Density estimation and reliable prediction regions for outputs are crucial in supervised and unsupervised learning. While conformal prediction effectively generates coverage-guaranteed regions, it struggles with multi-dimensional outputs due to reliance on one-dimensional nonconformity scores. To address this, we introduce CONTRA: CONformal prediction region via normalizing flow TRAnsformation. CONTRA utilizes the latent spaces of normalizing flows to define nonconformity scores based on distances from the center. This allows for the mapping of high-density regions in latent space to sharp prediction regions in the output space, surpassing traditional hyperrectangular or elliptical confor...
| Authors | Zhenhan Fang, Aixin Tan, Jian Huang |
|---|---|
| Primary category | stat.ML |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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