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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 571–580 of 704 papers

TitleStatusHype
Multi-Agent Reachability Calibration with Conformal Prediction—0
Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical AnalysisCode0
HappyMap: A Generalized Multi-calibration Method—0
Group conditional validity via multi-group learning—0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
Universal distribution of the empirical coverage in split conformal predictionCode0
Design-based conformal predictionCode0
Conformal Prediction for Network-Assisted Regression—0
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