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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 591–600 of 704 papers

TitleStatusHype
Conformal Prediction for STL Runtime Verification—0
Exact and Approximate Conformal Inference for Multi-Output Regression—0
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction—0
Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction—0
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
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