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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
Conformalised data synthesisCode0
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels—0
Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape TemplatesCode0
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification—0
Uncertainty quantification in automated valuation models with spatially weighted conformal predictionCode0
Conformal Prediction in Multi-User Settings: An EvaluationCode0
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction—0
Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal PredictionCode0
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees—0
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning—0
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