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 501–510 of 704 papers

TitleStatusHype
Adapting Conformal Prediction to Distribution Shifts Without Labels—0
Optimal Transport-based Conformal Prediction—0
Generative Conformal Prediction with Vectorized Non-Conformity Scores—0
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)—0
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction—0
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms—0
PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare—0
Transductive Conformal Inference for Full Ranking—0
Partial-Label Learning with Conformal Candidate Cleaning—0
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification—0
Show:102550
← PrevPage 51 of 71Next →

No leaderboard results yet.