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 421–430 of 704 papers

TitleStatusHype
Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression—0
Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision—0
Powerful batch conformal prediction for classification—0
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings—0
Predicting assisted ventilation in Amyotrophic Lateral Sclerosis using a mixture of experts and conformal predictors—0
Prediction and outlier detection in classification problems—0
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series—0
Model-free generalized fiducial inference—0
Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective—0
Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction—0
Show:102550
← PrevPage 43 of 71Next →

No leaderboard results yet.