Sequential Confidence Sets for Coverage-Constrained Conformal Model Selection
Modern conformal forecasting systems often maintain several adaptive pipelines that differ in base forecasters, conformity scores, calibration windows, and update rules. Comparing them is difficult because coverage is a hard constraint, whereas efficiency should be optimized only among feasible pipelines. We formulate this problem as sequential inference for a stochastic constrained argmin. At each time, the target is the set of minimum-cost pipelines satisfying multiple prefix-average conditional miscoverage constraints. We introduce Coverage-Constrained Sequential Model Confidence Sets (CC-SMCS), which separate certifiably feasible, possibly feasible, and possibly constrained-optimal pi...
| Authors | Jing Li, Haibin Zhu |
|---|---|
| Primary category | stat.ML |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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