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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 661–670 of 704 papers

TitleStatusHype
Efficient Conformal Prediction via Cascaded Inference with Expanded AdmissionCode1
Conformal Prediction Intervals for Neural Networks Using Cross Validation—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
Learning Robust Decision Policies from Observational Data—0
Conformal Prediction: a Unified Review of Theory and New ChallengesCode0
Training conformal predictors—0
Exchangeability, Conformal Prediction, and Rank Tests—0
Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction—0
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems—0
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