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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 501–510 of 704 papers

TitleStatusHype
Spatial Conformal Inference through Localized Quantile Regression—0
Spatio-Temporal Conformal Prediction for Power Outage Data—0
Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees—0
Conformalized Selective Regression—0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions—0
Conformalized Unconditional Quantile Regression—0
State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems—0
Statistical Guarantees in Data-Driven Nonlinear Control: Conformal Robustness for Stability and Safety—0
Statistical Guarantees in Synthetic Data through Conformal Adversarial Generation—0
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