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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 391–400 of 704 papers

TitleStatusHype
Conformalized Selective Regression—0
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-ValuesCode0
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks—0
Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction—0
Uncertainty-Aware Evaluation for Vision-Language ModelsCode1
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
TorchCP: A Python Library for Conformal PredictionCode3
Multi-View Conformal Learning for Heterogeneous Sensor FusionCode0
Conformalized Credal Set PredictorsCode0
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks—0
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