Length Optimization in Conformal Prediction

• URL: http://arxiv.org/abs/2406.18814v1
• Date: Thu, 27 Jun 2024 01:08:04 GMT
• Title: Length Optimization in Conformal Prediction
• Authors: Shayan Kiyani, George Pappas, Hamed Hassani,
• Abstract summary: We develop Conformal Prediction with Length-Optimization (CPL) as a principled framework for conformal prediction. CPL constructs prediction sets with (near-) optimal length while ensuring conditional validity. Our empirical evaluations demonstrate the superior prediction set size performance of CPL compared to state-of-the-art methods.
• Score: 22.733758606168873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conditional validity and length efficiency are two crucial aspects of conformal prediction (CP). Achieving conditional validity ensures accurate uncertainty quantification for data subpopulations, while proper length efficiency ensures that the prediction sets remain informative and non-trivial. Despite significant efforts to address each of these issues individually, a principled framework that reconciles these two objectives has been missing in the CP literature. In this paper, we develop Conformal Prediction with Length-Optimization (CPL) - a novel framework that constructs prediction sets with (near-) optimal length while ensuring conditional validity under various classes of covariate shifts, including the key cases of marginal and group-conditional coverage. In the infinite sample regime, we provide strong duality results which indicate that CPL achieves conditional validity and length optimality. In the finite sample regime, we show that CPL constructs conditionally valid prediction sets. Our extensive empirical evaluations demonstrate the superior prediction set size performance of CPL compared to state-of-the-art methods across diverse real-world and synthetic datasets in classification, regression, and text-related settings.

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