A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
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Updated
Sep 8, 2026 - Jupyter Notebook
A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
Lightweight, useful implementation of conformal prediction on real data.
A Library for Uncertainty Quantification.
Python package for conformal prediction
A Python toolbox for conformal prediction research on deep learning models, using PyTorch.
👋 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).
Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
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
Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.
Conformal prediction for time-series applications.
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
Official code for: Conformal prediction interval for dynamic time-series (conference, ICML 21 Long Presentation) AND Conformal prediction for time-series (journal, IEEE TPAMI)
the agi compiler: records llm agent behavior, proves what repeats, and compiles it into verified, sandboxed wasm binaries that run for microdollars. nothing figured out twice, paper: https://arxiv.org/abs/2607.04542
👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster
A Library for Conformal Hyperparameter Tuning
Valid and adaptive prediction intervals for probabilistic time series forecasting.
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