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 431–440 of 704 papers

TitleStatusHype
Conformalised data synthesisCode0
Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape TemplatesCode0
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification—0
Uncertainty quantification in automated valuation models with spatially weighted conformal predictionCode0
Conformal Prediction in Multi-User Settings: An EvaluationCode0
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction—0
Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal PredictionCode0
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees—0
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning—0
Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation AlgorithmsCode1
Show:102550
← PrevPage 44 of 71Next →

No leaderboard results yet.