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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 471–480 of 704 papers

TitleStatusHype
An Uncertainty-Aware Pseudo-Label Selection Framework using Regularized Conformal PredictionCode0
Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification—0
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
Robust Uncertainty Quantification Using Conformalised Monte Carlo PredictionCode1
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
Conformal PID Control for Time Series PredictionCode1
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