Treeffuser is an easy-to-use package for probabilistic prediction and probabilistic regression on tabular data with tree-based diffusion models.
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Updated
Sep 13, 2025 - Jupyter Notebook
Treeffuser is an easy-to-use package for probabilistic prediction and probabilistic regression on tabular data with tree-based diffusion models.
Official codebase for the paper "How to build a consistency model: Learning flow maps via self-distillation" (NeurIPS 2025).
A Julia package that provides (feedback) particle filters for nonlinear stochastic filtering and data assimilation problems
Code accompanying the paper 'Manifold MCMC methods for Bayesian inference in a wide class of diffusion models'
C++ implementation of statistics tools for stochastic models.
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