jakobzeitler Goto Github PK

ac-bo-hackathon.github.io

aciccomp

Code to run submissions for the Atlantic Causal Inference Competition

ada-lfs-download

Self-driving laboratory for accelerated discovery of thin-film materials - data repository

ada1929

Scripts for the Adafruit 1929 8x8 RGB board

ai-challenger

English-Chinese Machine Translation

aima-data

Automatically exported from code.google.com/p/aima-data

bcf

Bayesian Causal Forests

botorch

Bayesian optimization in PyTorch

botorchgeneraloptapp

causal_inference_python_code

Python code for part 2 of the book Causal Inference, by Miguel Hernán and James Robins

causalbayesianoptimization

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

causalinfo

Exploring Information Measures on Causal Graphs

cbed

Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.

counterfactual-fairness

Code for "Counterfactual Fairness" (NIPS2017)

daft

Render probabilistic graphical models using matplotlib

datasets

dmlongithub

dream4_exporter

elements-of-partial-identification

ember-rails

Ember for Rails 3.1+

fr-optapp

gesture-matching

grf

Generalized Random Forests

gryffin

interpretable-bo

As part of the AC Hackathon: Project 13: Interpretability of Bayesian Optimisation Campaigns

jakobzeitler.github.io

Jakob's personal page

joint_interventional_effects

Synthetic and semi-synthetic experiments for evaluating the approach to estimate joint interventional effects.

kerpy

python code for kernel methods

leaf

Open Machine Intelligence Framework

matterhornstudioflow-botorch