Dataset to assess the disentanglement properties of unsupervised learning methods

Course notes

[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts

Official Implementation of the paper "Variational Causal Networks: Approximate Bayesian Inference over Causal Structures"

Course notes for CS228: Probabilistic Graphical Models.

Code for the Causal Bayesian Optimization algorithm (http://proceedings.mlr.press/v108/aglietti20a/aglietti20a.pdf)

Presentation and implementation of reinforcement learning and bayesian RL techniques

Notebooks for Applied Causal Inference Powered by ML and AI

Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

Using reinforcement learning to sustain high-density populations in Conway’s Game of Life

An algorithm to calculate all pure strategy Nash equilibria in multi-objective games with quasiconvex utility functions

A Python implementation of game theory algorithms.

Multi-agent reinforcement learning programs based on Game theory

Game Theory Cheat Sheet

Matlab code for the figures and the examples used in G. Bacci, L. Sanguinetti, and M. Luise, "Understanding game theory via wireless power control,' submitted to IEEE Signal Process. Mag., Oct. 2014.

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…

Library for graphical models of decision making, based on pgmpy and networkx

A python library for 2 player games.

Conway's Game of Life, in PyTorch, using convolutions

Deep Reinforcement Learning Agent to control Conway's Game of Life

Connectivity-contrastive learning (CCL)

Code for "Generative causal explanations of black-box classifiers"

Codes for "CMVAE: Causal Meta VAE for Unsupervised Meta-Learning"

Official pytorch implementation codes for ECAI-2023 accepted paper "Causally Disentangled Generative Variational AutoEncoder"

disentanglement_lib is an open-source library for research on learning disentangled representations.