SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 171–180 of 704 papers

TitleStatusHype
Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach—0
Uncertainty Estimation for Path Loss and Radio Metric ModelsCode0
Seeing with Partial Certainty: Conformal Prediction for Robotic Scene Recognition in Built Environments—0
Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal TrainingCode0
Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs—0
Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide ImagesCode0
Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction—0
Adaptive Conformal Inference by Betting—0
Neural Conformal Control for Time Series ForecastingCode1
Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework—0
Show:102550
← PrevPage 18 of 71Next →

No leaderboard results yet.