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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 281–290 of 704 papers

TitleStatusHype
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose—0
CP-Router: An Uncertainty-Aware Router Between LLM and LRM—0
Conformalized Credal Regions for Classification with Ambiguous Ground Truth—0
CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions—0
Cross-Validation Conformal Risk Control—0
CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty—0
Conformalized Answer Set Prediction for Knowledge Graph Embedding—0
Data-Driven Personalized Energy Consumption Range Estimation for Plug-in Hybrid Electric Vehicles in Urban Traffic—0
Data-driven Reachability using Christoffel Functions and Conformal Prediction—0
Disturbance-Adaptive Data-Driven Predictive Control: Trading Comfort Violations for Savings in Building Climate Control—0
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