Conformal Prediction
147 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Conformal Prediction
Libraries
Use these libraries to find Conformal Prediction models and implementationsMost implemented papers
Uncertainty Sets for Image Classifiers using Conformal Prediction
aangelopoulos/conformal-classification
•
•
ICLR 2021
Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings.
Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
aangelopoulos/conformal-prediction
•
•
Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models.
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.
MAPIE: an open-source library for distribution-free uncertainty quantification
scikit-learn-contrib/mapie
•
•
Estimating uncertainties associated with the predictions of Machine Learning (ML) models is of crucial importance to assess their robustness and predictive power.
Conformal prediction interval for dynamic time-series
hamrel-cxu/EnbPI
•
•
We develop a method to construct distribution-free prediction intervals for dynamic time-series, called \Verb|EnbPI| that wraps around any bootstrap ensemble estimator to construct sequential prediction intervals.
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
Learning Optimal Conformal Classifiers
deepmind/conformal_training
•
•
ICLR 2022
However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets.
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Image-to-image regression is an important learning task, used frequently in biological imaging.
Adaptive Conformal Predictions for Time Series
While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency.
