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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 371–380 of 704 papers

TitleStatusHype
Mitigating LLM Hallucinations via Conformal Abstention—0
ACHO: Adaptive Conformal Hyperparameter Optimization—0
Model-Agnostic Uncertainty Quantification for Fast NFC Tag Identification using RF Fingerprinting—0
Predictive Inference with Weak Supervision—0
Conformal calibrators—0
Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs—0
Multi-Agent Reachability Calibration with Conformal Prediction—0
Multi-group Uncertainty Quantification for Long-form Text Generation—0
Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection—0
conformalClassification: A Conformal Prediction R Package for Classification—0
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