An extension of XGBoost to probabilistic modelling

Python package for conformal prediction

An extension of LightGBM to probabilistic modelling

Conformalized Quantile Regression

Quantile Regression Forests compatible with scikit-learn.

An extension of CatBoost to probabilistic modelling

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

👖 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.

Bringing back uncertainty to machine learning.

Official implementation of the paper "PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction" by Eli Simhayev, Gilad Katz and Lior Rokach.

Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.

Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation

Adaptive Conformal Prediction Intervals (ACPI) is a Python package that enhances the Predictive Intervals provided by the split conformal approach by employing a weighting strategy.

Prediction Intervals with specific value prediction

**curve_fit_utils** is a Python module containing useful tools for curve fitting

Implementation of Conformal Convolution T-learner (CCT) and Conformal Monte Carlo (CMC) learner

Deep joint mean and quantile regression for spatio-temporal problems

DualAQD: Dual Accuracy-quality-driven Prediction Intervals. IEEE TNNLS 2023.

An implementation of Self-Calibrating Conformal Prediction, accepted to Neurips 2024. SC-CP combines Venn-Abers calibration and conformal prediction to deliver calibrated point predictions alongside prediction intervals with finite-sample validity conditional on these predictions.