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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 121–130 of 704 papers

TitleStatusHype
Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints—0
Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification—0
Individualised Counterfactual Examples Using Conformal Prediction Intervals—0
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score—0
Scalable and adaptive prediction bands with kernel sum-of-squares—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
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction—0
Optimal Conformal Prediction under Epistemic UncertaintyCode0
Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction—0
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