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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 91–100 of 704 papers

TitleStatusHype
Minimum Volume Conformal Sets for Multivariate RegressionCode1
Online Selective Conformal Prediction: Errors and Solutions—0
NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis PredictionCode0
CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions—0
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal PredictionCode0
Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading—0
Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal predictionCode0
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
E-Values Expand the Scope of Conformal PredictionCode0
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression—0
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