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Conformal Prediction: A Data Perspective
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Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework, reliably provides valid predictive inference for black-box models. CP constructs prediction sets that contain the true output with a specified probability. However, modern data science diverse modalities, along with increasing data and model complexity, challenge traditional CP methods. These developments have spurred novel approaches to address evolving scenarios. This survey reviews the foundational concepts of CP and recent advancements from a data-centric perspective, including applications to structured, unstructured, and dynamic data. We also discuss the challenges and opportunities CP faces in large-scale data and models.
Forward citations
Cited by 3 Pith papers
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A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression
New CDF-based and latent-space conformity scores give multi-output conformal predictors asymptotic conditional coverage while retaining finite-sample marginal coverage.
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WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts
WQLCP weights calibration samples by VAE reconstruction losses and scales test scores by a test-loss quantile to improve conformal prediction under shifts, but the algorithm is ill-defined and the empirical support is weak.
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Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction
Conformal prediction gives valid and compact label sets on a seven-class aerial event recognition task with fewer than 400 training samples, and temperature scaling sometimes increases set size.
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