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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 511–520 of 704 papers

TitleStatusHype
Physics Constrained Motion Prediction with Uncertainty Quantification—0
Posterior Conformal Prediction—0
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging—0
Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression—0
Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision—0
Powerful batch conformal prediction for classification—0
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning—0
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings—0
Predicting assisted ventilation in Amyotrophic Lateral Sclerosis using a mixture of experts and conformal predictors—0
Prediction and outlier detection in classification problems—0
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