johaupt

bayesian_methods

Exercises on Bayesian methods for machine learning

C50varimp

Calculate mean decrease in entropy as a variable importance metric for C5.0 boosted trees in R

GANbalanced

johaupt.github.io

pyswarm

Particle swarm optimization (PSO) that supports constraints

response-dependent-costs

Code accompanying the paper "Customer targeting under response-dependent costs"

supervised_randomization

Experiments showing the profit efficiency of targeted randomized sampling in comparison to standard A/B testing

treatment-learn

utils

johaupt/GANbalanced

johaupt/bayesian_methods

Exercises on Bayesian methods for machine learning

johaupt/treatment-learn

johaupt/johaupt.github.io

johaupt/supervised_randomization

Experiments showing the profit efficiency of targeted randomized sampling in comparison to standard A/B testing

johaupt/response-dependent-costs

Code accompanying the paper "Customer targeting under response-dependent costs"

johaupt/C50varimp

Calculate mean decrease in entropy as a variable importance metric for C5.0 boosted trees in R

johaupt/pyswarm

Particle swarm optimization (PSO) that supports constraints

johaupt/utils

johaupt/causalDNN

johaupt/deep-conditional-distribution-regression

Python package for the deep distribution regression method.

johaupt/imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

johaupt/wgan-gp

Pytorch implementation of Wasserstein GANs with Gradient Penalty

johaupt/XBCF

R and python implementations of Accelerated Bayesian Causal Forest.