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.
Conformal Prediction: A Data Perspective
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.