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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 131–140 of 704 papers

TitleStatusHype
A Cross-Conformal Predictor for Multi-label Classification—0
Concepts and Applications of Conformal Prediction in Computational Drug Discovery—0
Approximating Score-based Explanation Techniques Using Conformal Regression—0
A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning—0
Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers—0
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets—0
Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Applying Regression Conformal Prediction with Nearest Neighbors to time series data—0
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI—0
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