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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 531–540 of 704 papers

TitleStatusHype
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects—0
Approximating Score-based Explanation Techniques Using Conformal Regression—0
How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled AutonomyCode0
Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces—0
Conformance Testing for Stochastic Cyber-Physical Systems—0
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction—0
Probabilistically robust conformal prediction—0
Conformal prediction for frequency-severity modelingCode0
Fairness Under Demographic Scarce RegimeCode0
Model-free generalized fiducial inference—0
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