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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 526–550 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
Conformal Prediction for Time Series with Modern Hopfield NetworksCode1
Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty PropagationCode1
Adaptive Conformal Prediction by Reweighting Nonconformity ScoreCode1
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical AnalysisCode0
HappyMap: A Generalized Multi-calibration Method—0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
Group conditional validity via multi-group learning—0
Universal distribution of the empirical coverage in split conformal predictionCode0
Design-based conformal predictionCode0
Improving Adaptive Conformal Prediction Using Self-Supervised LearningCode1
Conformal Prediction for Network-Assisted Regression—0
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal PredictionCode0
One-Shot Federated Conformal PredictionCode1
From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling—0
Fortuna: A Library for Uncertainty Quantification in Deep LearningCode2
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk ControlCode1
Physics Constrained Motion Prediction with Uncertainty Quantification—0
Conformal Prediction for Trustworthy Detection of Railway Signals—0
Conformal Loss-Controlling Prediction—0
Conformal Prediction Intervals for Remaining Useful Lifetime EstimationCode1
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging—0
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