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 111–120 of 704 papers

TitleStatusHype
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces—0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
Statistical Guarantees in Data-Driven Nonlinear Control: Conformal Robustness for Stability and Safety—0
Efficient Robust Conformal Prediction via Lipschitz-Bounded NetworksCode0
Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees—0
Conformal coronary calcification volume estimation with conditional coverage via histogram clustering—0
JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows—0
Conformal Object Detection by Sequential Risk Control—0
Individualised Counterfactual Examples Using Conformal Prediction Intervals—0
Test-time augmentation improves efficiency in conformal prediction—0
Show:102550
← PrevPage 12 of 71Next →

No leaderboard results yet.