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 191–200 of 704 papers

TitleStatusHype
Conformalized Unconditional Quantile Regression—0
Conformal Prediction for Distribution-free Optimal Control of Linear Stochastic Systems—0
Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market—0
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Applying Regression Conformal Prediction with Nearest Neighbors to time series data—0
Conformal Prediction for Hierarchical Data—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions—0
Online Calibrated and Conformal Prediction Improves Bayesian Optimization—0
Show:102550
← PrevPage 20 of 71Next →

No leaderboard results yet.