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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 521–530 of 704 papers

TitleStatusHype
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine LearningCode0
Conformal Off-Policy Evaluation in Markov Decision Processes—0
Conformalized Unconditional Quantile Regression—0
Development and Evaluation of Conformal Prediction Methods for QSAR—0
Conformal Prediction Regions for Time Series using Linear Complementarity ProgrammingCode0
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
Conformal Prediction for Time Series with Modern Hopfield NetworksCode1
Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty PropagationCode1
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