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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 541–550 of 704 papers

TitleStatusHype
Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guarantees—0
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach—0
Uncertainty-Aware Decarbonization for Datacenters—0
Regression Conformal Prediction with Nearest Neighbours—0
Relational Conformal Prediction for Correlated Time Series—0
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
A Conformal Prediction Approach to Explore Functional Data—0
Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures—0
Conformal Contextual Robust Optimization—0
Conformal coronary calcification volume estimation with conditional coverage via histogram clustering—0
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