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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 211–220 of 704 papers

TitleStatusHype
Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction—0
Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions—0
Conformal Prediction in Dynamic Biological Systems—0
Conformal Prediction in Hierarchical Classification—0
Online Calibrated and Conformal Prediction Improves Bayesian Optimization—0
An Information Theoretic Perspective on Conformal Prediction—0
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets—0
Conformal Prediction with Upper and Lower Bound Models—0
Conformal Prediction Intervals for Markov Decision Process Trajectories—0
Conformal Predictive Portfolio Selection—0
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