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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 631–640 of 704 papers

TitleStatusHype
Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction SetsCode0
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical AnalysisCode0
Informativeness of Weighted Conformal PredictionCode0
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language ModelsCode0
Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor MappingCode0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
Leave-One-Out Stable Conformal PredictionCode0
Length Optimization in Conformal PredictionCode0
Less is More: Rejecting Unreliable Reviews for Product Question AnsweringCode0
Marginal and training-conditional guarantees in one-shot federated conformal predictionCode0
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