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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 561–570 of 704 papers

TitleStatusHype
Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees—0
Conformal Nucleus Sampling—0
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling—0
Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision—0
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine LearningCode0
Conformal Regression in Calorie Prediction for Team Jumbo-Visma—0
Conformal Off-Policy Evaluation in Markov Decision Processes—0
Conformalized Unconditional Quantile Regression—0
Development and Evaluation of Conformal Prediction Methods for QSAR—0
Conformal Prediction Regions for Time Series using Linear Complementarity ProgrammingCode0
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