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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 291–300 of 704 papers

TitleStatusHype
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
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging—0
Conformalized Credal Regions for Classification with Ambiguous Ground Truth—0
A Collaborative Content Moderation Framework for Toxicity Detection based on Conformalized Estimates of Annotation DisagreementCode0
Multi-model Ensemble Conformal Prediction in Dynamic EnvironmentsCode0
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI—0
Powerful batch conformal prediction for classification—0
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