Split Localized Conformal Prediction
Conformal prediction is a simple and powerful tool that can quantify uncertainty without any distributional assumptions. Many existing methods only address the average coverage guarantee, which is not ideal compared to the stronger conditional coverage guarantee. Existing methods of approximating conditional coverage require additional models or time effort, which makes them not easy to scale. In this paper, we propose a modified non-conformity score by leveraging the local approximation of the conditional distribution using kernel density estimation. The modified score inherits the spirit of split conformal methods, which is simple and efficient and can scale to high dimensional settings. We also proposed a unified framework that brings together our method and several state-of-the-art. We perform extensive empirical evaluations: results measured by both average and conditional coverage confirm the advantage of our method.
Code (1)
Tasks
Conformal PredictionDensity EstimationPredictionPrediction IntervalsregressionSimilar Papers 제목 키워드 기반
MD-split+: Practical Local Conformal Inference in High Dimensions
Quantifying uncertainty in model predictions is a common goal for practitioners seeking more than just point predictions. One tool for uncertainty quantification that requires minimal assumptions is conformal inference, …
Conformal PredictionDensity EstimationOpen-Ended Question AnsweringPrediction+3Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction
Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscalibration, while exact distribution-free c…
Cluster Frequency Conformal Prediction for Local Coverage
Conformal prediction provides distribution-free coverage guarantees, but in many-class classification it may still under-cover specific classes or subpopulations, preventing safe deployment in high-stakes applications. W…
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning
Bayesian deep learning and conformal prediction are two methods that have been used to convey uncertainty and increase safety in machine learning systems. We focus on combining Bayesian deep learning with split conformal…
Conformal PredictionDeep Learningimage-classificationImage Classification+2Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regress…
Conformal PredictionPredictionPrediction Intervalsregression+1