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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 361–370 of 704 papers

TitleStatusHype
Fair Uncertainty Quantification for Depression Prediction—0
Stochastic Model Predictive Control of Charging Energy Hubs with Conformal Prediction—0
Stochastic Online Conformal Prediction with Semi-Bandit Feedback—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction—0
Strategic Conformal Prediction—0
Conformalized Strategy-Proof Auctions—0
Conformal prediction of future insurance claims in the regression problem—0
Flow-based Conformal Prediction for Multi-dimensional Time Series—0
Calibrated Physics-Informed Uncertainty Quantification—0
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