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 371–380 of 704 papers

TitleStatusHype
Formal Verification and Control with Conformal Prediction—0
Bridging conformal prediction and scenario optimization—0
Foundation models for time series forecasting: Application in conformal prediction—0
From Conformal Predictions to Confidence Regions—0
From conformal to probabilistic prediction—0
From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling—0
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning—0
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose—0
Streamlining Conformal Information Retrieval via Score Refinement—0
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging—0
Show:102550
← PrevPage 38 of 71Next →

No leaderboard results yet.