modular domain generalization: https://pypi.org/project/domainlab/

Causal Discovery in Python. Learning causality from data.

A modular, easy to extend GFlowNet library

A collection of visual guides to help applied scientists learn causal inference.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

A collection of resources and papers on Diffusion Models

Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper

Monte Carlo tree search in JAX

Line-by-line profiling for Python

MAGIC (Markov Affinity-based Graph Imputation of Cells), is a method for imputing missing values restoring structure of large biological datasets.

Figure sizes, font sizes, fonts, and more configurations at minimal overhead. Fix your journal papers, conference proceedings, and other scientific publications.

Examples of how to create colorful, annotated equations in Latex using Tikz.

Neural Networks and Deep Learning, NUS CS5242, 2021

A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.

Multi-Joint dynamics with Contact. A general purpose physics simulator.

Official data and code for our paper Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

This is the repository for the distill web framework

Python Toolkit for Causal and Probabilistic Reasoning

Uplift modeling and causal inference with machine learning algorithms

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

Proximal optimization in pure python

Express & compile probabilistic programs for performant inference on CPU & GPU. Powered by JAX.

Evaluation Framework for Probabilistic Programming Languages