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 161–170 of 704 papers

TitleStatusHype
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction—0
Statistical Guarantees in Synthetic Data through Conformal Adversarial Generation—0
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning—0
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
SConU: Selective Conformal Uncertainty in Large Language Models—0
Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor MappingCode0
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios—0
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer—0
Leave-One-Out Stable Conformal PredictionCode0
Show:102550
← PrevPage 17 of 71Next →

No leaderboard results yet.