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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 501–510 of 704 papers

TitleStatusHype
Max-Rank: Efficient Multiple Testing for Conformal Prediction—0
Robust Conformal Prediction for STL Runtime Verification under Distribution ShiftCode0
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification FrameworkCode0
Early-Exit Neural Networks with Nested Prediction Sets—0
Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts—0
Conformalized Deep Splines for Optimal and Efficient Prediction SetsCode0
Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records—0
High-Dimensional Prediction for Sequential Decision Making—0
CBD: A Certified Backdoor Detector Based on Local Dominant ProbabilityCode0
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks—0
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