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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 671–680 of 704 papers

TitleStatusHype
Conformal Prediction with Learned Features—0
Closing the Loop on Runtime Monitors with Fallback-Safe MPC—0
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models—0
Conformal Prediction with Temporal Quantile Adjustments—0
Conformal Prediction with Upper and Lower Bound Models—0
Conformal Predictive Portfolio Selection—0
Conformal Predictive Programming for Chance Constrained Optimization—0
CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators—0
Conformal Predictors for Compound Activity Prediction—0
Data-light Uncertainty Set Merging with Admissibility: Synthetics, Aggregation, and Test Inversion—0
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