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Conformal Prediction

Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.

Papers

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Showing 61–70 of 704 papers

TitleStatusHype
Real-Time Privacy Preservation for Robot Visual Perception—0
Fair Uncertainty Quantification for Depression Prediction—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weightingCode0
Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees—0
Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures—0
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction—0
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
Statistical Guarantees in Synthetic Data through Conformal Adversarial Generation—0
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning—0
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