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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 671–680 of 704 papers

TitleStatusHype
Robust Conformal Prediction Using Privileged InformationCode0
Robust Conformal Volume Estimation in 3D Medical ImagesCode0
Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAPCode0
Robust Yet Efficient Conformal Prediction SetsCode0
Root-finding Approaches for Computing Conformal Prediction SetCode0
Safety-Critical Control with Offline-Online Neural Network InferenceCode0
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal PredictionCode0
Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal PredictionCode0
Similarity-Navigated Conformal Prediction for Graph Neural NetworksCode0
Sparse Activations as Conformal PredictorsCode0
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