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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 571–580 of 704 papers

TitleStatusHype
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks—0
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners—0
Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target—0
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction—0
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
Uncertainty-Guided Enhancement on Driving Perception System via Foundation Models—0
Conformalized Link Prediction on Graph Neural Networks—0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
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