A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.
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Updated
Sep 17, 2024 - Jupyter Notebook
A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.
Lightweight, useful implementation of conformal prediction on real data.
Conformalized Quantile Regression
A Library for Uncertainty Quantification.
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).
Python package for conformal prediction
Various Conformal Prediction methods implemented from scratch in pure NumPy for an educational purpose.
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
Official code for: Conformal prediction interval for dynamic time-series (conference, ICML 21 Long Presentation) AND Conformal prediction for time-series (journal, IEEE TPAMI)
Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc
👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.
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.
Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms
Valid and adaptive prediction intervals for probabilistic time series forecasting
Conformal Prediction - A Practical Guide with MAPIE
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)
Conformal prediction for time-series applications.
Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vision and natural language processing.
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
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