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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 631–640 of 704 papers

TitleStatusHype
Online Calibrated and Conformal Prediction Improves Bayesian Optimization—0
Conformal prediction for text infilling and part-of-speech predictionCode0
Applying Regression Conformal Prediction with Nearest Neighbors to time series data—0
Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis—0
Learning Pareto-Efficient Decisions with Confidence—0
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning—0
Improving Prediction Confidence in Learning-Enabled Autonomous Systems—0
Trading Coverage for Precision: Conformal Prediction with Limited False Discoveries—0
Adversarially Robust Conformal Prediction—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
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