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Leave-One-Out Stable Conformal Prediction
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Leave-One-Out Stable Conformal Prediction
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Conformal prediction (CP) is an important tool for distribution-free predictive uncertainty quantification. Yet, a major challenge is to balance computational efficiency and prediction accuracy, particularly for multiple predictions. We propose Leave-One-Out Stable Conformal Prediction (LOO-StabCP), a novel method to speed up full conformal using algorithmic stability without sample splitting. By leveraging leave-one-out stability, our method is much faster in handling a large number of prediction requests compared to existing method RO-StabCP based on replace-one stability. We derived stability bounds for several popular machine learning tools: regularized loss minimization (RLM) and stochastic gradient descent (SGD), as well as kernel method, neural networks and bagging. Our method is theoretically justified and demonstrates superior numerical performance on synthetic and real-world data. We applied our method to a screening problem, where its effective exploitation of training data led to improved test power compared to state-of-the-art method based on split conformal.
Forward citations
Cited by 6 Pith papers
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Approximating full conformal prediction: distribution free guarantees via the tournament correction
Tournament corrections approximate full conformal prediction while providing rigorous marginal coverage of 1-2α (tightening to ~1-α under stability).
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Approximating full conformal prediction: distribution free guarantees via the tournament correction
Introduces a tournament correction framework for approximating full conformal prediction with rigorous 1-2α marginal coverage guarantees that generalize leave-one-out cross-conformal prediction.
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