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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 321–330 of 704 papers

TitleStatusHype
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification—0
Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction—0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—0
α-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction—0
Conformal Prediction for Trustworthy Detection of Railway Signals—0
Probabilistic Conformal Prediction with Approximate Conditional Validity—0
Conditional Conformal Risk Adaptation—0
Are foundation models for computer vision good conformal predictors?—0
Adversarially Robust Conformal Prediction—0
Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets—0
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