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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 681–690 of 704 papers

TitleStatusHype
Conformal Regression in Calorie Prediction for Team Jumbo-Visma—0
Cautious Deep Learning—0
Scalable and adaptive prediction bands with kernel sum-of-squares—0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
Conformal Robust Beamforming via Generative Channel Models—0
SConU: Selective Conformal Uncertainty in Large Language Models—0
Conformal Rule-Based Multi-label Classification—0
Conformal Safety Shielding for Imperfect-Perception Agents—0
Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework—0
Conformal Temporal Logic Planning using Large Language Models—0
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