Conformal Prediction
64 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Conformal Prediction
Most implemented papers
Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
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.
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.
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.
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.
Conformal prediction set for time-series
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.
Model-Robust Counterfactual Prediction Method
We develop a novel method for counterfactual analysis based on observational data using prediction intervals for units under different exposures.
Multi-class probabilistic classification using inductive and cross Venn-Abers predictors
Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems.
libconform v0.1.0: a Python library for conformal prediction
This paper introduces libconform v0. 1. 0, a Python library for the conformal prediction framework, licensed under the MIT-license.
