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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 261–270 of 704 papers

TitleStatusHype
Conformalized Link Prediction on Graph Neural Networks—0
A Conformal Approach to Feature-based Newsvendor under Model Misspecification—0
Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
Data-driven Reachability using Christoffel Functions and Conformal Prediction—0
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Android Malware Detection with Unbiased Confidence Guarantees—0
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction—0
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