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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 401–425 of 704 papers

TitleStatusHype
Task-Oriented Mulsemedia Communication using Unified Perceiver and Conformal Prediction in 6G Wireless Systems—0
Improved conformalized quantile regression—0
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction—0
Improve ROI with Causal Learning and Conformal Prediction—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
Improving Coverage in Combined Prediction Sets with Weighted p-values—0
Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Conformal Prediction Sets—0
Improving Prediction Confidence in Learning-Enabled Autonomous Systems—0
Testing for concept shift online—0
Test-time augmentation improves efficiency in conformal prediction—0
WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts—0
Individualised Counterfactual Examples Using Conformal Prediction Intervals—0
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures—0
Validity, consonant plausibility measures, and conformal prediction—0
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning—0
JANET: Joint Adaptive predictioN-region Estimation for Time-series—0
JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows—0
Joint Registration and Conformal Prediction for Partially Observed Functional Data—0
KACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network for Heart Disease Detection—0
Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage—0
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components—0
Kernel-based Optimally Weighted Conformal Prediction Intervals—0
Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction—0
Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework—0
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