Conformal Prediction

130 papers with code • 0 benchmarks • 0 datasets

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Most 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

yromano/cqr NeurIPS 2019

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 15 Jul 2021

Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models.

MAPIE: an open-source library for distribution-free uncertainty quantification

scikit-learn-contrib/mapie 25 Jul 2022

Estimating uncertainties associated with the predictions of Machine Learning (ML) models is of crucial importance to assess their robustness and predictive power.

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

kwuthrich/scinference 25 Dec 2017

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.

Conformal prediction interval for dynamic time-series

hamrel-cxu/EnbPI 18 Oct 2020

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

zhimeir/cfsurvival 17 Mar 2021

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.

Adaptive Conformal Predictions for Time Series

mzaffran/adaptiveconformalpredictionstimeseries 15 Feb 2022

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.

Conformal prediction set for time-series

hamrel-cxu/ensemble-regularized-adaptive-prediction-set-eraps 15 Jun 2022

When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods.