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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 191–200 of 704 papers

TitleStatusHype
E-Values Expand the Scope of Conformal PredictionCode0
Mirror Online Conformal Prediction with Intermittent Feedback—0
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression—0
Enhanced Route Planning with Calibrated Uncertainty Set—0
Model-Agnostic Uncertainty Quantification for Fast NFC Tag Identification using RF Fingerprinting—0
Conformal Prediction and Human Decision Making—0
Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework—0
Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds—0
Conformal Prediction for Image Segmentation Using Morphological Prediction SetsCode0
Robust Conformal Prediction with a Single Binary Certificate—0
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