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 461–470 of 704 papers

TitleStatusHype
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-ValuesCode0
Conformalized Selective Regression—0
Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction—0
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks—0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Multi-View Conformal Learning for Heterogeneous Sensor FusionCode0
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks—0
Conformalized Credal Set PredictorsCode0
Language Models with Conformal Factuality Guarantees—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Show:102550
← PrevPage 47 of 71Next →

No leaderboard results yet.