How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing
In classification-oriented adaptive sensing, posterior samples characterize uncertainty at the current measurement state and can serve two roles: they may guide the next sensing direction, while their class labels provide votes for the candidate classes and determine whether sensing should continue. We focus on the stopping layer that turns these votes into a declaration, without modifying the posterior sampler or sensing directions. A natural plug-in rule declares when the observed vote share exceeds a threshold. We show that this threshold is not itself a confidence guarantee: when the underlying vote mass equals the threshold, the plug-in rule declares about half the time. As alternati...
| Authors | Vincent Corlay, Andriy Enttsel |
|---|---|
| Primary category | stat.ML |
| Published | 18 September 2026 |
| Last updated at source | 30 September 2026 |
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