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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 51–75 of 704 papers

TitleStatusHype
Reachability Barrier Networks: Learning Hamilton-Jacobi Solutions for Smooth and Flexible Control Barrier Functions—0
A Physics-Informed Convolutional Long Short Term Memory Statistical Model for Fluid Thermodynamics SimulationsCode0
A Conformal Predictive Measure for Assessing Catastrophic Forgetting—0
SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation—0
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse ProblemsCode0
Robust Indoor Localization via Conformal Methods and Variational Bayesian Adaptive Filtering—0
Validation of Conformal Prediction in Cervical Atypia Classification—0
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events—0
Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting ModelCode0
Real-Time Privacy Preservation for Robot Visual Perception—0
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Fair Uncertainty Quantification for Depression Prediction—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
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor MappingCode0
SConU: Selective Conformal Uncertainty in Large Language Models—0
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios—0
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer—0
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