CoCoNet: Convex Conformal Network for Multi-Dimensional Uncertainty Regions
Conformal prediction (CP) provides distribution-free coverage guarantees under exchangeability, but the efficiency of its prediction regions depends critically on the choice of the conformity score. This is particularly important in multivariate regression, where dependencies across output dimensions induce complex joint uncertainty structures. This paper introduces CoCoNet (Convex Conformal Network) for learning convex conformity scores that induce flexible, data-adaptive prediction regions. CoCoNet learns the score from held-out data by optimizing prediction-region efficiency while enforcing convexity with respect to the residual. CoCoNet then applies split-conformal calibration to reta...
| Authors | Alireza Moradi, Thomas Bruys, Pascal Van Hentenryck et al. |
|---|---|
| Primary category | stat.ML |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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