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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 311–320 of 704 papers

TitleStatusHype
Conformal Prediction Intervals for Markov Decision Process Trajectories—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components—0
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets—0
Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel Service Industry—0
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery—0
Assumption-free fidelity bounds for hardware noise characterization—0
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
Conformal Prediction in Hierarchical Classification—0
Conformal Prediction in Dynamic Biological Systems—0
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