An extension of XGBoost to probabilistic modelling

An extension of LightGBM to probabilistic modelling

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Mambular is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

An extension of CatBoost to probabilistic modelling

Distributional Gradient Boosting Machines

A python package for semi-structured deep distributional regression

An extension of Py-Boost to probabilistic modelling

Code of "Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts", Pic et al. (2024+)

Code for the KDD 2019 workshop paper. Attention mechanism for distribution regression.

Time Series based Ensemble Model Output Statistics

Framework for the visualization of distributional regression models

code for the KDD 2019 workshop paper https://arxiv.org/abs/1904.10583. Kernel mean embedding for distribution regression.

Penalized Transformation Models in Liesel

Bayesian Conditional Transformation Models by Manuel Carlan, Thomas Kneib and Nadja Klein