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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 476–500 of 704 papers

TitleStatusHype
Anomalous Edge Detection in Edge Exchangeable Social Network Models—0
Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction—0
Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction—0
Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors—0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Prediction—0
On Temperature Scaling and Conformal Prediction of Deep Classifiers—0
One Sample is Enough to Make Conformal Prediction Robust—0
An Information Theoretic Perspective on Conformal Prediction—0
Trading Coverage for Precision: Conformal Prediction with Limited False Discoveries—0
Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading—0
Online Multivalid Learning: Means, Moments, and Prediction Intervals—0
Online scalable Gaussian processes with conformal prediction for guaranteed coverage—0
Online Selective Conformal Prediction: Errors and Solutions—0
On the Construction of Distribution-Free Prediction Intervals for an Image Regression Problem in Semiconductor Manufacturing—0
Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction—0
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning—0
Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms—0
On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction—0
Training-Conditional Coverage Bounds under Covariate Shift—0
On the Utility of Prediction Sets in Human-AI Teams—0
On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling—0
On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals—0
Training conformal predictors—0
On Uncertainty In Natural Language Processing—0
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