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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 421–430 of 704 papers

TitleStatusHype
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction—0
Forecasting CPI inflation under economic policy and geopolitical uncertaintiesCode1
SymmPI: Predictive Inference for Data with Group SymmetriesCode0
Efficient Conformal Prediction under Data Heterogeneity—0
Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions—0
Android Malware Detection with Unbiased Confidence Guarantees—0
Faithful Model Explanations through Energy-Constrained Conformal CounterfactualsCode0
Reliable Prediction Intervals with Regression Neural Networks—0
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels—0
Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction—0
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