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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 491–500 of 704 papers

TitleStatusHype
Uncertainty Quantification of the Virial Black Hole Mass with Conformal PredictionCode0
TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal PredictionCode0
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners—0
Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data—0
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction—0
UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation—0
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement LearningCode1
Conformal Language ModelingCode1
Class-Conditional Conformal Prediction with Many ClassesCode1
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding—0
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