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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 341–350 of 704 papers

TitleStatusHype
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Applying Regression Conformal Prediction with Nearest Neighbors to time series data—0
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift—0
Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market—0
Conformal Prediction for Distribution-free Optimal Control of Linear Stochastic Systems—0
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees—0
Max-Rank: Efficient Multiple Testing for Conformal Prediction—0
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI—0
A Cross-Conformal Predictor for Multi-label Classification—0
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