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 171–180 of 704 papers

TitleStatusHype
Conformal Object Detection by Sequential Risk Control—0
Conformal Off-Policy Evaluation in Markov Decision Processes—0
Causal Responder Detection—0
Conformal Off-Policy Prediction for Multi-Agent Systems—0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
Cautious Deep Learning—0
Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction—0
Conformal Prediction for Trustworthy Detection of Railway Signals—0
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces—0
Conformalizing Machine Translation Evaluation—0
Show:102550
← PrevPage 18 of 71Next →

No leaderboard results yet.