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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 471–480 of 704 papers

TitleStatusHype
Early-Exit Neural Networks with Nested Prediction Sets—0
Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction—0
Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective—0
Neurosymbolic Conformal Classification—0
Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction—0
Anomalous Edge Detection in Edge Exchangeable Social Network Models—0
Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction—0
Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction—0
Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors—0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
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