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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 101–110 of 704 papers

TitleStatusHype
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning—0
A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions—0
Conformal Inductive Graph Neural Networks—0
Conformalized Answer Set Prediction for Knowledge Graph Embedding—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components—0
Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel Service Industry—0
Assumption-free fidelity bounds for hardware noise characterization—0
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
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