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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 451–460 of 704 papers

TitleStatusHype
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction—0
Retrain or not retrain: Conformal test martingales for change-point detection—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays—0
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners—0
Android Malware Detection with Unbiased Confidence Guarantees—0
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model—0
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning—0
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