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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 601–610 of 704 papers

TitleStatusHype
Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks—0
Conformal Off-Policy Prediction for Multi-Agent Systems—0
Conformal Off-Policy Prediction in Contextual Bandits—0
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling—0
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty—0
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee—0
Confidence Calibration for Systems with Cascaded Predictive Modules—0
Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts—0
Conformal Prediction: A Data Perspective—0
Conformal Prediction and Human Decision Making—0
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