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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 81–90 of 704 papers

TitleStatusHype
Conformal Robust Beamforming via Generative Channel Models—0
Conformal Data-driven Control of Stochastic Multi-Agent Systems under Collaborative Signal Temporal Logic SpecificationsCode0
Learning-Based Conformal Tube MPC for Safe Control in Interactive Multi-Agent SystemsCode1
Distributionally Robust Predictive Runtime Verification under Spatio-Temporal Logic SpecificationsCode0
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification—0
Stochastic Model Predictive Control of Charging Energy Hubs with Conformal Prediction—0
Disturbance-adaptive Model Predictive Control for Bounded Average Constraint Violations—0
Bridging conformal prediction and scenario optimization—0
Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
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