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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
Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage—0
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components—0
Kernel-based Optimally Weighted Conformal Prediction Intervals—0
Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction—0
Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework—0
Language Models with Conformal Factuality Guarantees—0
Assumption-free fidelity bounds for hardware noise characterization—0
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees—0
α-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction—0
Learning Cellular Network Connection Quality with Conformal—0
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