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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 101–110 of 704 papers

TitleStatusHype
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression—0
Mirror Online Conformal Prediction with Intermittent Feedback—0
Conformal Prediction and Human Decision Making—0
Model-Agnostic Uncertainty Quantification for Fast NFC Tag Identification using RF Fingerprinting—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
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning—0
Robust Conformal Prediction with a Single Binary Certificate—0
Conformal Prediction for Image Segmentation Using Morphological Prediction SetsCode0
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