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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 151–160 of 704 papers

TitleStatusHype
Early-Exit Neural Networks with Nested Prediction Sets—0
Conformal Prediction Bands for Two-Dimensional Functional Time Series—0
Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction—0
Cautious Deep Learning—0
Adaptive Uncertainty Quantification for Generative AI—0
Conformal Prediction based Spectral Clustering—0
Conformal Prediction for Distribution-free Optimal Control of Linear Stochastic Systems—0
Causal Responder Detection—0
Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Prediction—0
Adaptive Temperature Scaling with Conformal Prediction—0
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