<ul><li>Conformal prediction and scenario optimization are two statistical learning frameworks used to certify decisions made using data.</li><li>While these frameworks have been extensively studied and yield similar results, no clear connection between them has been established.</li><li>This research focuses on vanilla conformal prediction and demonstrates how to choose appropriate score functions and set predictor maps to recover bounds on the probability of constraint violation associated with scenario programs.</li><li>The study also establishes a theoretical bridge between conformal prediction and scenario optimization, allowing for analysis of calibration conditional conformal prediction.</li></ul>

Bridging conformal prediction and scenario optimization

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