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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 181–190 of 704 papers

TitleStatusHype
Bridging conformal prediction and scenario optimization—0
Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
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
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal PredictionCode0
Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal predictionCode0
CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions—0
Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading—0
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