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 351–360 of 704 papers

TitleStatusHype
Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction—0
Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework—0
Language Models with Conformal Factuality Guarantees—0
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees—0
Learning Cellular Network Connection Quality with Conformal—0
A Conformal Predictive Measure for Assessing Catastrophic Forgetting—0
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling—0
Learning Pareto-Efficient Decisions with Confidence—0
Learning Robust Decision Policies from Observational Data—0
Learning Temporal Logic Predicates from Data with Statistical Guarantees—0
Show:102550
← PrevPage 36 of 71Next →

No leaderboard results yet.