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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 461–470 of 704 papers

TitleStatusHype
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access—0
Multi-group Uncertainty Quantification for Long-form Text Generation—0
Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection—0
Adaptive Conformal Regression with Jackknife+ Rescaled Scores—0
When Can We Reuse a Calibration Set for Multiple Conformal Predictions?—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
Multivariate Conformal Prediction using Optimal Transport—0
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction—0
Adaptive Conformal Inference by Betting—0
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction—0
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