A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.

A Library for Uncertainty Quantification.

Lightweight, useful implementation of conformal prediction on real data.

Python package for conformal prediction

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.

Conformalized Quantile Regression

Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

Various Conformal Prediction methods implemented from scratch in pure NumPy for an educational purpose.

Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc

Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.

Conformal prediction for time-series applications.

Official code for: Conformal prediction interval for dynamic time-series (conference, ICML 21 Long Presentation) AND Conformal prediction for time-series (journal, IEEE TPAMI)

👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster

Valid and adaptive prediction intervals for probabilistic time series forecasting

Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)

Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vision and natural language processing.

[ NeurIPS 2023 ] Official Codebase for "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects"