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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 201–210 of 704 papers

TitleStatusHype
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series—0
Human-Calibrated Automated Testing and Validation of Generative Language Models—0
Conformal Prediction for Hierarchical Data—0
Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed Energy Adoption—0
Theoretical Foundations of Conformal Prediction—0
Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction—0
Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control—0
Conformalized Credal Regions for Classification with Ambiguous Ground Truth—0
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging—0
Multi-model Ensemble Conformal Prediction in Dynamic EnvironmentsCode0
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