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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 561–570 of 704 papers

TitleStatusHype
Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction—0
Bayesian Optimization with Conformal Prediction SetsCode1
Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction—0
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction—0
Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled ExamplesCode0
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal PredictionCode0
Conformalized Fairness via Quantile RegressionCode0
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov ChainsCode0
Predictive Inference with Feature Conformal PredictionCode1
Batch Multivalid Conformal PredictionCode1
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