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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 391–400 of 704 papers

TitleStatusHype
HappyMap: A Generalized Multi-calibration Method—0
Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework—0
Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed Energy Adoption—0
High-Dimensional Prediction for Sequential Decision Making—0
UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation—0
Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict—0
Human-Calibrated Automated Testing and Validation of Generative Language Models—0
Validation of Conformal Prediction in Cervical Atypia Classification—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions—0
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