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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 601–610 of 704 papers

TitleStatusHype
Distributionally Robust Predictive Runtime Verification under Spatio-Temporal Logic SpecificationsCode0
Distribution-Free Calibration of Statistical Confidence SetsCode0
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point ProcessesCode0
Distribution Free Prediction Sets for Node ClassificationCode0
Efficient and Differentiable Conformal Prediction with General Function ClassesCode0
Efficient Robust Conformal Prediction via Lipschitz-Bounded NetworksCode0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal TrainingCode0
Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized ModelsCode0
Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreakCode0
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