CAOS: Conformal Aggregation of One-Shot Predictors

• URL: http://arxiv.org/abs/2601.05219v1
• Date: Thu, 08 Jan 2026 18:44:21 GMT
• Title: CAOS: Conformal Aggregation of One-Shot Predictors
• Authors: Maja Waldron,
• Abstract summary: One-shot prediction enables rapid adaptation of pretrained foundation models to new tasks using only one labeled example.<n>Standard split conformal methods are inefficient in the one-shot setting due to data splitting and reliance on a single predictor.<n>We propose Conformal Aggregation of One-Shot Predictors (CAOS), a conformal framework that adaptively aggregates multiple one-shot predictors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One-shot prediction enables rapid adaptation of pretrained foundation models to new tasks using only one labeled example, but lacks principled uncertainty quantification. While conformal prediction provides finite-sample coverage guarantees, standard split conformal methods are inefficient in the one-shot setting due to data splitting and reliance on a single predictor. We propose Conformal Aggregation of One-Shot Predictors (CAOS), a conformal framework that adaptively aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme to fully exploit scarce labeled data. Despite violating classical exchangeability assumptions, we prove that CAOS achieves valid marginal coverage using a monotonicity-based argument. Experiments on one-shot facial landmarking and RAFT text classification tasks show that CAOS produces substantially smaller prediction sets than split conformal baselines while maintaining reliable coverage.

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