تقرير
Length Optimization in Conformal Prediction
العنوان: | Length Optimization in Conformal Prediction |
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المؤلفون: | Kiyani, Shayan, Pappas, George, Hassani, Hamed |
سنة النشر: | 2024 |
المجموعة: | Computer Science Statistics |
مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Methodology |
الوصف: | 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. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2406.18814 |
رقم الأكسشن: | edsarx.2406.18814 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |