Code for the KDD 2019 workshop paper. Attention mechanism for distribution regression.
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Updated
Sep 9, 2019 - Jupyter Notebook
Code for the KDD 2019 workshop paper. Attention mechanism for distribution regression.
code for the KDD 2019 workshop paper https://arxiv.org/abs/1904.10583. Kernel mean embedding for distribution regression.
A python package for semi-structured deep distributional regression
Distributional Gradient Boosting Machines
An extension of Py-Boost to probabilistic modelling
Bayesian Conditional Transformation Models by Manuel Carlan, Thomas Kneib and Nadja Klein
Framework for the visualization of distributional regression models
An extension of CatBoost to probabilistic modelling
Time Series based Ensemble Model Output Statistics
An extension of LightGBM to probabilistic modelling
Code of "Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts", Pic et al. (2024+)
An extension of XGBoost to probabilistic modelling
Penalized Transformation Models in Liesel
Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. This includes models like Mambular, FT-Transformer, TabTransformer and tabular ResNets.
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