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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

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Showing 321–330 of 704 papers

TitleStatusHype
Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions—0
Efficient Conformal Prediction under Data Heterogeneity—0
Bridging conformal prediction and scenario optimization—0
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer—0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Efficient Online Set-valued Classification with Bandit Feedback—0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data—0
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
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