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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 661–670 of 704 papers

TitleStatusHype
Weighted Aggregation of Conformity Scores for Classification—0
Conformal Predictions for Longitudinal Data—0
Conformal Predictions under Markovian Data—0
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets—0
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees—0
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