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 481–490 of 704 papers

TitleStatusHype
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models—0
Data-light Uncertainty Set Merging with Admissibility: Synthetics, Aggregation, and Test Inversion—0
Scalable and adaptive prediction bands with kernel sum-of-squares—0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
SConU: Selective Conformal Uncertainty in Large Language Models—0
Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework—0
Seeing with Partial Certainty: Conformal Prediction for Robotic Scene Recognition in Built Environments—0
Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds—0
Selecting informative conformal prediction sets with false coverage rate control—0
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems—0
Show:102550
← PrevPage 49 of 71Next →

No leaderboard results yet.