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 301–310 of 704 papers

TitleStatusHype
Efficiency of conformalized ridge regression—0
Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders—0
Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts—0
Conformalized Link Prediction on Graph Neural Networks—0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction—0
Distribution-Free Guarantees for Systems with Decision-Dependent Noise—0
Distribution-Free Matrix Prediction Under Arbitrary Missing Pattern—0
Distribution-free Conformal Prediction for Ordinal Classification—0
CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition—0
Show:102550
← PrevPage 31 of 71Next →

No leaderboard results yet.