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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 31–40 of 704 papers

TitleStatusHype
Scalable and adaptive prediction bands with kernel sum-of-squares—0
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction—0
CP-Router: An Uncertainty-Aware Router Between LLM and LRM—0
WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts—0
Optimal Conformal Prediction under Epistemic UncertaintyCode0
Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction—0
A Generic Framework for Conformal FairnessCode0
Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAPCode0
Multivariate Latent Recalibration for Conditional Normalizing FlowsCode0
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings—0
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