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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 171–180 of 704 papers

TitleStatusHype
Conditional Conformal Risk Adaptation—0
FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair—0
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging—0
Assumption-free fidelity bounds for hardware noise characterization—0
Conformal Robust Beamforming via Generative Channel Models—0
Conformal Data-driven Control of Stochastic Multi-Agent Systems under Collaborative Signal Temporal Logic SpecificationsCode0
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification—0
Distributionally Robust Predictive Runtime Verification under Spatio-Temporal Logic SpecificationsCode0
Stochastic Model Predictive Control of Charging Energy Hubs with Conformal Prediction—0
Disturbance-adaptive Model Predictive Control for Bounded Average Constraint Violations—0
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