A scikit-learn-compatible module for estimating prediction intervals.
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Updated
Jun 6, 2024 - Jupyter Notebook
A scikit-learn-compatible module for estimating prediction intervals.
A Library for Uncertainty Quantification.
Lightweight, useful implementation of conformal prediction on real data.
Conformal classifiers, regressors and predictive systems
👋 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
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.
Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
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)
Valid and adaptive prediction intervals for probabilistic time series forecasting
👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster
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"
Conditional calibration of conformal p-values for outlier detection.
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