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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 441–450 of 704 papers

TitleStatusHype
Rectifying Conformity Scores for Better Conditional Coverage—0
Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guarantees—0
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach—0
Conformalized Credal Regions for Classification with Ambiguous Ground Truth—0
Regression Conformal Prediction with Nearest Neighbours—0
Relational Conformal Prediction for Correlated Time Series—0
Conformalized Decision Risk Assessment—0
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout—0
Reliable Prediction Intervals with Regression Neural Networks—0
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks—0
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