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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 441–450 of 704 papers

TitleStatusHype
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding—0
Likelihood-Ratio Regularized Quantile Regression: Adapting Conformal Prediction to High-Dimensional Covariate Shifts—0
Approximating Score-based Explanation Techniques Using Conformal Regression—0
Making learning more transparent using conformalized performance prediction—0
Applying Regression Conformal Prediction with Nearest Neighbors to time series data—0
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering—0
Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces—0
MD-split+: Practical Local Conformal Inference in High Dimensions—0
Meta-Analysis with Untrusted Data—0
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction—0
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