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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 261–270 of 704 papers

TitleStatusHype
Seeing with Partial Certainty: Conformal Prediction for Robotic Scene Recognition in Built Environments—0
Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal TrainingCode0
Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs—0
Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide ImagesCode0
Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction—0
Adaptive Conformal Inference by Betting—0
Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework—0
A Conformal Approach to Feature-based Newsvendor under Model Misspecification—0
On the Role of Surrogates in Conformal Inference of Individual Causal EffectsCode0
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems—0
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